The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees – EEOC
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This technical assistance document was issued upon approval of the Chair of the U.S. Equal Employment Opportunity Commission.
The contents of this document do not have the force and effect of law and are not meant to bind the public in any way. This document is intended only to provide clarity to the public regarding existing requirements under the law or agency policies.
Employers now have a wide variety of computer-based tools available to assist them in hiring workers, monitoring worker performance, determining pay or promotions, and establishing the terms and conditions of employment. Employers may utilize these tools in an attempt to save time and effort, increase objectivity, or decrease bias. However, the use of these tools may disadvantage job applicants and employees with disabilities. When this occurs, employers may risk violating federal Equal Employment Opportunity (“EEO”) laws that protect individuals with disabilities.
The Questions and Answers in this document explain how employers’ use of software that relies on algorithmic decision-making may violate existing requirements under Title I of the Americans with Disabilities Act (“ADA”). This technical assistance also provides practical tips to employers on how to comply with the ADA, and to job applicants and employees who think that their rights may have been violated.
The Equal Employment Opportunity Commission (“EEOC” or “the Commission”) enforces, and provides leadership and guidance on, the federal EEO laws prohibiting employment discrimination on the basis of race, color, national origin, religion, and sex (including pregnancy, sexual orientation, and gender identity), disability, age (over 40) and genetic information. This publication is part of an ongoing effort by the EEOC to educate employers, employees, and other stakeholders about the application of EEO laws when employers use employment software and applications, some of which incorporate algorithmic decision-making.
As a starting point, this section explains the meaning of three, central terms used in this document—software, algorithms, and artificial intelligence (“AI”) —and how, when used in a workplace, they relate to each other.
There are many different types of software and applications used in employment, including: automatic resume-screening software, hiring software, chatbot software for hiring and workflow, video interviewing software, analytics software, employee monitoring software, and worker management software.
Employers may rely on different types of software that incorporate algorithmic decision-making at a number of stages of the employment process. Examples include: resume scanners that prioritize applications using certain keywords; employee monitoring software that rates employees on the basis of their keystrokes or other factors; “virtual assistants” or “chatbots” that ask job candidates about their qualifications and reject those who do not meet pre-defined requirements; video interviewing software that evaluates candidates based on their facial expressions and speech patterns; and testing software that provides “job fit” scores for applicants or employees regarding their personalities, aptitudes, cognitive skills, or perceived “cultural fit” based on their performance on a game or on a more traditional test. Each of these types of software may include AI.
1. What is the ADA and how does it define “disability”?
The ADA is a federal civil rights law. Title I of the ADA prohibits employers, employment agencies, labor organizations, and joint labor-management committees with 15 or more employees from discriminating on the basis of disability. Other parts of the ADA, not discussed here, ensure that people with disabilities have full access to public and private services and facilities.
The ADA has a very specific definition of a current “disability.” A physical or mental impairment meets the ADA’s definition of a current “disability” if it would, when left untreated, “substantially limit” one or more “major life activities.” Major life activities include, for example, seeing, reaching, communicating, speaking, concentrating, or the operation of major bodily functions, such as brain or neurological functions. (There are two other definitions of “disability” that are not the subject of this discussion. For more information on the definition of “disability” under the ADA, see EEOC’s Questions and Answers on the ADA Amendments Act.)
A condition does not need to be permanent or severe, or cause a high degree of functional limitation, to be “substantially limiting.” It may qualify as substantially limiting, for example, by making activities more difficult, painful, or time-consuming to perform as compared to the way that most people perform them. In addition, if the symptoms of the condition come and go, the condition still will qualify as a disability if it substantially limits a major life activity when active. Many common and ordinary medical conditions will qualify.
2. How could an employer’s use of algorithmic decision-making tools violate the ADA?
The most common ways that an employer’s use of algorithmic decision-making tools could violate the ADA are:
An employer’s use of an algorithmic decision-making tool may be unlawful for one of the above reasons, or for several such reasons.
3. Is an employer responsible under the ADA for its use of algorithmic decision-making tools even if the tools are designed or administered by another entity, such as a software vendor?
In many cases, yes. For example, if an employer administers a pre-employment test, it may be responsible for ADA discrimination if the test discriminates against individuals with disabilities, even if the test was developed by an outside vendor. In addition, employers may be held responsible for the actions of their agents, which may include entities such as software vendors, if the employer has given them authority to act on the employer’s behalf.
4. What is a reasonable accommodation?
A reasonable accommodation is a change in the way things are done that helps a job applicant or employee with a disability apply for a job, do a job, or enjoy equal benefits and privileges of employment. Examples of reasonable accommodations may include specialized equipment, alternative tests or testing formats, permission to work in a quiet setting, and exceptions to workplace policies. These are just examples—almost any change can be a reasonable accommodation—although an employer never has to lower production or performance standards or eliminate an essential job function as a reasonable accommodation.
5. May an employer announce generally (or use software that announces generally) that reasonable accommodations are available to job applicants and employees who are asked to use or be evaluated by an algorithmic decision-making tool, and invite them to request reasonable accommodations when needed?
Yes. An employer may tell applicants or employees what steps an evaluation process includes and may ask them whether they will need reasonable accommodations to complete it. For example, if a hiring process includes a video interview, the employer or software vendor may tell applicants that the job application process will involve a video interview and provide a way to request a reasonable accommodation. Doing so is a “promising practice” to avoid violating the ADA.
6. When an employer uses algorithmic decision-making tools to assess job applicants or employees, does the ADA require the employer to provide reasonable accommodations?
If an applicant or employee tells the employer that a medical condition may make it difficult to take a test, or that it may cause an assessment result that is less acceptable to the employer, the applicant or employee has requested a reasonable accommodation. To request an accommodation, it is not necessary to mention the ADA or use the phrase “reasonable accommodation.”
Under the ADA, employers need to respond promptly to requests for reasonable accommodation. If it is not obvious or already known whether the requesting applicant or employee has an ADA disability and needs a reasonable accommodation because of it, the employer may request supporting medical documentation. When the documentation shows that a disability might make a test more difficult to take or that it might reduce the accuracy of an assessment, the employer must provide an alternative testing format or a more accurate assessment of the applicant’s or employee’s skills as a reasonable accommodation, unless doing so would involve significant difficulty or expense (also called “undue hardship”).
For example, a job applicant who has limited manual dexterity because of a disability may report that they would have difficulty taking a knowledge test that requires the use of a keyboard, trackpad, or other manual input device. Especially if the responses are timed, this kind of test will not accurately measure this particular applicant’s knowledge. In this situation, the employer would need to provide an accessible version of the test (for example, one in which the applicant is able to provide responses orally, rather than manually) as a reasonable accommodation, unless doing so would cause undue hardship. If it is not possible to make the test accessible, the ADA requires the employer to consider providing an alternative test of the applicant’s knowledge as a reasonable accommodation, barring undue hardship.
Other examples of reasonable accommodations that may be effective for some individuals with disabilities include extended time or an alternative version of the test, including one that is compatible with accessible technology (like a screen-reader) if the applicant or employee uses such technology. Employers must give individuals receiving reasonable accommodation equal consideration with other applicants or employees not receiving reasonable accommodations.
The ADA requires employers to keep all medical information obtained in connection with a request for reasonable accommodation confidential and must store all such information separately from the applicant’s or employee’s personnel file.
7. Is an employer responsible for providing reasonable accommodations related to the use of algorithmic decision-making tools, even if the software or application is developed or administered by another entity?
In many cases, yes. As explained in Question 3 above, an employer may be held responsible for the actions of other entities, such as software vendors, that the employer has authorized to act on its behalf. For example, if an employer were to contract with a software vendor to administer and score on its behalf a pre-employment test, the employer likely would be held responsible for actions that the vendor performed—or did not perform—on its behalf. Thus, if an applicant were to tell the vendor that a medical condition was making it difficult to take the test (which qualifies as a request for reasonable accommodation), and the vendor did not provide an accommodation that was required under the ADA, the employer likely would be responsible even if it was unaware that the applicant reported a problem to the vendor.
8. When is an individual “screened out” because of a disability, and when is screen out potentially unlawful?
Screen out occurs when a disability prevents a job applicant or employee from meeting—or lowers their performance on—a selection criterion, and the applicant or employee loses a job opportunity as a result. The ADA says that screen out is unlawful if the individual who is screened out is able to perform the essential functions of the job with a reasonable accommodation if one is legally required.[1] Questions 9 and 10 explain the meaning of “screen out” and Question 11 provides examples of when a person who is screened out due to a disability nevertheless can do the job with a reasonable accommodation.
9. Could algorithmic decision-making tools screen out an individual because of a disability? What are some examples?
Yes, an algorithmic decision-making tool could screen out an individual because of a disability if the disability causes that individual to receive a lower score or an assessment result that is less acceptable to the employer, and the individual loses a job opportunity as a result.
An example of screen out might involve a chatbot, which is software designed to engage in communications online and through texts and emails. A chatbot might be programmed with a simple algorithm that rejects all applicants who, during the course of their “conversation” with the chatbot, indicate that they have significant gaps in their employment history. If a particular applicant had a gap in employment, and if the gap had been caused by a disability (for example, if the individual needed to stop working to undergo treatment), then the chatbot may function to screen out that person because of the disability.
Another kind of screen out may occur if a person’s disability prevents the algorithmic decision-making tool from measuring what it is intended to measure. For example, video interviewing software that analyzes applicants’ speech patterns in order to reach conclusions about their ability to solve problems is not likely to score an applicant fairly if the applicant has a speech impediment that causes significant differences in speech patterns. If such an applicant is rejected because the applicant’s speech impediment resulted in a low or unacceptable rating, the applicant may effectively have been screened out because of the speech impediment.
10. Some algorithmic decision-making tools may say that they are “bias-free.” If a particular tool makes this claim, does that mean that the tool will not screen out individuals with disabilities?
When employers (or entities acting on their behalf such as software vendors) say that they have designed an algorithmic decision-making tool to be “bias-free,” it typically means that they have taken steps to prevent a type of discrimination known as “adverse impact” or “disparate impact” discrimination under Title VII, based on race, sex, national origin, color, or religion. This type of Title VII discrimination involves an employment policy or practice that has a disproportionately negative effect on a group of individuals who share one of these characteristics, like a particular race or sex.[2]
To reduce the chances that the use of an algorithmic decision-making tool results in disparate impact discrimination on bases like race and sex, employers and vendors sometimes use the tool to assess subjects in different demographic groups, and then compare the average results for each group. If the average results for one demographic group are less favorable than those of another (for example, if the average results for individuals of a particular race are less favorable than the average results for individuals of a different race), the tool may be modified to reduce or eliminate the difference.
The steps taken to avoid that kind of Title VII discrimination are typically distinct from the steps needed to address the problem of disability bias.[3] If an employer or vendor were to try to reduce disability bias in the way described above, doing so would not mean that the algorithmic decision-making tool could never screen out an individual with a disability. Each disability is unique. An individual may fare poorly on an assessment because of a disability, and be screened out as a result, regardless of how well other individuals with disabilities fare on the assessment. Therefore, to avoid screen out, employers may need to take different steps beyond the steps taken to address other forms of discrimination. (See Question 12.)
11. Screen out because of a disability is unlawful if the individual who is screened out is able to perform the essential functions of the job, with a reasonable accommodation if one is legally required. If an individual is screened out by an algorithmic decision-making tool, is it still possible that the individual is able to perform the essential functions of the job?
In some cases, yes. For example, some employers rely on “gamified” tests, which use video games to measure abilities, personality traits, and other qualities, to assess applicants and employees. If a business requires a 90 percent score on a gamified assessment of memory, an applicant who is blind and therefore cannot play these particular games would not be able to score 90 percent on the assessment and would be rejected. But the applicant still might have a very good memory and be perfectly able to perform the essential functions of a job that requires a good memory.
Even an algorithmic decision-making tool that has been “validated” for some purposes might screen out an individual who is able to perform well on the job. To say that a decision-making tool has been “validated”[4] means that there is evidence meeting certain professional standards showing that the tool accurately measures or predicts a trait or characteristic that is important for a specific job. Algorithmic decision-making tools may be validated in this sense, and still be inaccurate when applied to particular individuals with disabilities. For example, the gamified assessment of memory may be validated because it has been shown to be an accurate measure of memory for most people in the general population, yet still screen out particular individuals who have good memories but are blind, and who therefore cannot see the computer screen to play the games.
An algorithmic decision-making tool also may sometimes screen out individuals with disabilities who could do the job because the tool does not take into account the possibility that such individuals are entitled to reasonable accommodations on the job. Algorithmic decision-making tools are often designed to predict whether applicants can do a job under typical working conditions. But people with disabilities do not always work under typical conditions if they are entitled to on-the-job reasonable accommodations.
For example, some pre-employment personality tests are designed to look for candidates who are similar to the employer’s most successful employees—employees who most likely work under conditions that are typical for that employer. Someone who has Posttraumatic Stress Disorder (“PTSD”) might be rated poorly by one of these tests if the test measures a trait that may be affected by that particular individual’s PTSD, such as the ability to ignore distractions. Even if the test is generally valid and accurately predicts that this individual would have difficulty handling distractions under typical working conditions, it might not accurately predict whether the individual still would experience those same difficulties under modified working conditions—specifically, conditions in which the employer provides required on-the-job reasonable accommodations such as a quiet workstation or permission to use noise-cancelling headphones. If such a person were to apply for the job and be screened out because of a low score on the distraction test, the screen out may be unlawful under the ADA. Some individuals who may test poorly in certain areas due to a medical condition may not even need a reasonable accommodation to perform a job successfully.
12. What could an employer do to reduce the chances that algorithmic decision-making tools will screen out someone because of a disability, even though that individual is able to perform the essential functions of the job (with a reasonable accommodation if one is legally required)?
First, if an employer is deciding whether to rely on an algorithmic decision-making tool developed by a software vendor, it may want to ask the vendor whether the tool was developed with individuals with disabilities in mind. Some possible inquiries about the development of the tool that an employer might consider include, but are not limited to:
If an employer is developing its own algorithmic decision-making tool, it could reduce the chances of unintentional screen out by taking the same considerations into account during its development process. Depending on the type of tool in question, reliance on experts on various types of disabilities throughout the development process may be effective. For example, if an employer is developing pre-employment tests that measure personality, cognitive, or neurocognitive traits, it may be helpful to employ psychologists, including neurocognitive psychologists, throughout the development process in order to spot ways in which the test may screen out people with autism or cognitive, intellectual, or mental health-related disabilities.
Second, regardless of whether the employer or another entity is developing an algorithmic decision-making tool, the employer may be able to take additional steps during implementation and deployment to reduce the chances that the tool will screen out someone because of a disability, either intentionally or unintentionally. Such steps include:
Taking these steps will provide individuals with disabilities an opportunity to decide whether a reasonable accommodation may be necessary. For example, suppose that an employer uses an algorithm to evaluate its employees’ productivity, and the algorithm takes into account the employee’s average number of keystrokes per minute. If the employer does not inform its employees that it is using this algorithm, an employee who is blind or has a visual impairment and who uses voice recognition software instead of a keyboard may be rated poorly and lose out on a promotion or other job opportunity as a result. If the employer informs its employees that they will be assessed partly on the basis of keyboard usage, however, that same employee would know to request an alternative means of measuring productivity—perhaps one that takes into account the use of voice recognition software rather than keystrokes—as a reasonable accommodation.
Another way for employers to avoid ADA discrimination when using algorithmic decision-making tools is to try to ensure that no one is screened out unless they are unable to do the job, even when provided with reasonable accommodations. A promising practice is to only develop and select tools that measure abilities or qualifications that are truly necessary for the job—even for people who are entitled to an on-the-job reasonable accommodation. For example, an employer who is hiring cashiers might want to ensure that the chatbot software it is using does not reject applicants who are unable to stand for long periods. Otherwise, a chatbot might reject an applicant who uses a wheelchair and may be entitled to a lowered cash register as a reasonable accommodation.
As a further measure, employers may wish to avoid using algorithmic decision-making tools that do not directly measure necessary abilities and qualifications for performing a job, but instead make inferences about those abilities and qualifications based on characteristics that are correlated with them. For example, if an open position requires the ability to write reports, the employer may wish to avoid algorithmic decision-making tools that rate this ability by measuring the similarity between an applicant’s personality and the typical personality for currently successful report writers. By doing so, the employer lessens the likelihood of rejecting someone who is good at writing reports, but whose personality, because of a disability, is uncommon among successful report writers.
13. How could an employer’s use of algorithmic decision-making tools violate ADA restrictions on disability-related inquiries and medical examinations?
An employer might violate the ADA if it uses an algorithmic decision-making tool that poses “disability-related inquiries” or seeks information that qualifies as a “medical examination” before giving the candidate a conditional offer of employment.[5] This type of violation may occur even if the individual does not have a disability.
An assessment includes “disability-related inquiries” if it asks job applicants or employees questions that are likely to elicit information about a disability or directly asks whether an applicant or employee is an individual with disability. It qualifies as a “medical examination” if it seeks information about an individual’s physical or mental impairments or health.
An algorithmic decision-making tool that could be used to identify an applicant’s medical conditions would violate these restrictions if it were administered prior to a conditional offer of employment. Not all algorithmic decision-making tools that ask for health-related information are “disability-related inquiries or medical examinations,” however. For example, a personality test is not posing “disability-related inquiries” because it asks whether the individual is “described by friends as being ‘generally optimistic,’” even if being described by friends as generally optimistic might somehow be related to some kinds of mental health diagnoses.
Note, however, that even if a request for health-related information does not violate the ADA’s restrictions on disability-related inquiries and medical examinations, it still might violate other parts of the ADA. For example, if a personality test asks questions about optimism, and if someone with Major Depressive Disorder (“MDD”) answers those questions negatively and loses an employment opportunity as a result, the test may “screen out” the applicant because of MDD. As explained in Questions 8–11 above, such screen out may be unlawful if the individual who is screened out can perform the essential functions of the job, with or without reasonable accommodation.
Once employment has begun, disability-related inquiries may be made and medical examinations may be required only if they are legally justified under the ADA.
For more information on disability-related inquiries and medical examinations, see Pre-Employment Inquiries and Medical Questions & Examinations, and Enforcement Guidance on Disability-Related Inquiries and Medical Examinations of Employees under the ADA.
14. What can employers do to comply with the ADA when using algorithmic decision-making tools?
15. What should I do to ensure that I am being assessed fairly by algorithmic decision-making tools?
If you have a medical condition that you think might qualify as an ADA disability and that could negatively affect the results of an evaluation performed by algorithmic decision-making tools, you may want to begin by asking for details about the employer’s use of such tools to determine if it might pose any problems related to your disability. If so, you may want to ask for a reasonable accommodation that allows you to compete on equal footing with other applicants or employees.
For example, if an employer’s hiring process includes a test, you may wish to ask for an accessible format or an alternative test that measures your ability to do the job in a way that is not affected by your disability. To request a reasonable accommodation, you need to notify an employer representative or official (for example, someone in Human Resources) or, if the employer is contracting with a software vendor, the vendor’s representative or the employer, that you have a medical condition, and that you need something changed because of the medical condition to ensure that your abilities are evaluated accurately.
Note that if your disability and need for accommodation are not obvious or already known, you may be asked to submit some medical documentation in support of your request for accommodation. To find out more about asking for reasonable accommodations, see Enforcement Guidance on Reasonable Accommodation and Undue Hardship under the ADA, available at https://www.eeoc.gov/laws/guidance/enforcement-guidance-reasonable-accommodation-and-undue-hardship-under-ada.
If you only discover that an algorithmic decision-making tool poses a problem due to your disability after the evaluation process is underway, you should notify the employer or software vendor as soon as you are aware of the problem and ask to be evaluated in a way that accurately reflects your ability to do the job, with a reasonable accommodation if one is legally required.
If you have already received a poor rating generated by an employer’s use of an algorithmic decision-making tool, you should think about whether your health condition might have prevented you from achieving a higher rating. For example, might a disability have negatively affected the results of an assessment, or made it impossible for you to complete an assessment? If so, you could contact the employer or software vendor immediately, explain the disability-related problem, and ask to be reassessed using a different format or test, or to explain how you could perform at a high level despite your performance on the test.
16. What do I do if I think my rights have been violated?
If you believe that your employment-related ADA rights may have been violated, the EEOC can help you decide what to do next. For example, if the employer or software vendor refuses to consider your request for a reasonable accommodation to take or re-take a test, and if you think that you would be able to do the job with a reasonable accommodation, you might consider filing a charge of discrimination with the EEOC. A discrimination charge is an applicant’s or employee’s statement alleging that an employer engaged in employment discrimination and asking the EEOC to help find a remedy under the EEO laws.
If you file a charge of discrimination, the EEOC will conduct an investigation. Mediation, which is an informal and confidential way for people to resolve disputes with the help of a neutral mediator, may also be available. Because you must file an EEOC charge within 180 days of the alleged violation in order to take further legal action (or 300 days if the employer is also covered by a state or local employment discrimination law), it is best to begin the process early. It is unlawful for an employer to retaliate against you for contacting the EEOC or filing a charge.
If you would like to begin the process of filing a charge, go to our Online Public Portal at https://publicportal.eeoc.gov, visit your local EEOC office (see https://www.eeoc.gov/field-office for contact information), or contact us by phone at 1-800-669-4000 (voice), 1-800-669-6820 (TTY), or 1-844-234-5122 (ASL Video Phone).
For general information, visit the EEOC’s website (https://www.eeoc.gov).
This information is not new policy; rather, this document applies principles already established in the ADA’s statutory and regulatory provisions as well as previously issued guidance. The contents of this publication do not have the force and effect of law and are not meant to bind the public in any way. This publication is intended only to provide clarity to the public regarding existing requirements under the law. As with any charge of discrimination filed with the EEOC, the Commission will evaluate alleged ADA violations involving the use of software, algorithms, and artificial intelligence based on all of the facts and circumstances of the particular matter and applicable legal principles.
[1] To establish a screen out claim, the individual alleging discrimination must show that the challenged selection criterion screens out or tends to screen out an individual with a disability or a class of individuals with disabilities. See 42 U.S.C. § 12112(b)(6); 29 C.F.R. § 1630.10(a). To establish a defense, the employer must demonstrate that the challenged application of the criterion is “job related and consistent with business necessity,” as that term is understood under the ADA, and that “such performance cannot be accomplished by reasonable accommodation.” 42 U.S.C. §§ 12112(b)(6), 12113(a); 29 C.F.R. §§ 1630.10(a), 1630.15(b); 29 C.F.R. pt. 1630 app. §§ 1630.10, 1630.15 (b) and (c). A different defense to a claim that a selection criterion screens out or tends to screen out an individual with a disability or a class of individuals with disabilities is available when the challenged selection criterion is safety-based. See 2 U.S.C. § 12113(b); 29 C.F.R. § 1630.15(b)(2).
[2] 42 U.S.C. § 2000e-2(a)(2), (k).
[3] When applying the tool to current employees or other subjects, there will generally be no way to know who has a disability and who does not.
[4] When employers or vendors claims that a tool designed to help employers decide which job applicants to hire has been “validated,” or that such a tool is a “valid predictor” of job performance, they may mean that there is evidence that the tool measures a trait or characteristic that is important for the job, and that the evidence meets the standards articulated in the Uniform Guidelines on Employee Selection Procedures (“UGESP”), 29 C.F.R. §§ 1607.5–9. UGESP articulates standards for compliance with certain requirements under Title VII. UGESP does not apply to disability discrimination. 29 C.F.R. pt. 1630 app. § 1630.10 (a) (“The Uniform Guidelines on Employee Selection Procedures . . . do not apply to the Rehabilitation Act and are similarly inapplicable to this part.”).
[5] Note, however, that the ADA permits employers to request reasonable medical documentation in support of a request for reasonable accommodation, when necessary. This may be done prior to a conditional offer of employment if the request is for a reasonable accommodation that is needed to help the individual complete the job application process.
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The Best Photo Scanners for 2022 in the UAE and Saudi Arabia – PCMag Middle East
Both photo buffs and family archivists often turn to photo scanners to digitize their prints and film. Photo scanners are set apart by features such as a high sensor resolution and the ability to scan transparencies (slides and negatives) in addition to photo prints. Many include software to help retouch scans and remove scratches.
Though built to handle photos, most photo scanners can also be used for general-purpose scanning, and some include optical character recognition (OCR) software for digitizing the occasional document. This is a niche segment of the hardware market; you’ll see relatively few dedicated photo scanners on the market, and they tend to have very long shelf lives. (As you peruse and click on our models summed up above and below, you’ll note that many were reviewed years ago. Yes, they’re still the current models.) Check out summaries of our still-current favorites below, followed by a guide to choosing the right model for you, and then a detailed spec table of all our recommended models.
Why We Picked It
The Canon CanoScan LiDE 400 is an under-$100, entry-level flatbed photo scanner. But it fills a nifty niche for nonprofessional photographers who need to scan both photos and, from time to time, documents. Since it doesn’t have an automatic document feeder (ADF), it’s not your best choice for frequent multipage jobs, but it combines document management and optical character recognition (OCR) with impressive photo scanning and touch-up software. It also saves desk space with a vertical kickstand and can stitch together multiple scans of plus-size photos.
Who It’s For
The LiDE 400 lacks Wi-Fi or wired networking, but it capably serves a personal or small-office PC connected via USB. Its vibrant color and crisp detail make it a great pick for light-duty photo-quality scanning.
Why We Picked It
Got a shoebox full of snapshots? You need Epson’s FastFoto FF-680W, whose automatic document feeder (ADF) is specially designed to handle stacks of prints. (It has plastic guides with marks for 5-by-7- and 4-by-6-inch originals, and it can adjust to other sizes.) It feeds through stacks of pics without tearing or creasing them as a text-oriented scanner’s feeder might. It’s much quicker than manually placing photos on a flatbed scanner’s glass, one after the other, and it does a decent job of turning document pages into searchable PDF or Word files when you’ve emptied the shoebox.
Who It’s For
Home archivists who want to digitize stacks and scrapbooks full of photo prints will think the FastFoto FF-680W is heaven-sent. It’s smart about scanning both sides of postcards and other items (ignoring flip sides if they’re blank). And it can help you rotate, crop, or restore images or upload them to Dropbox or Google Drive.
Why We Picked It
Want higher-quality scans of photo prints than your typical multifunction printer/copier/scanner can provide, but don’t need to scan film? Do not pass Go, but head straight to the Epson Perfection V39, an affordable flatbed photo scanner that also scans to editable text and searchable PDF formats. Its 2,400ppi resolution is fine-grained enough to let you crop in on and enlarge a small section of a photo. And it gives you a choice of an easy automatic mode, or granular settings such as a Color Restore mode that boosts the color of faded photos.
Who It’s For
With a single USB cable for both power and data and a semi-vertical kickstand that saves desk space, the Perfection V39 is simple to operate, and it outshines its competitors at also handling the occasional short document. Both casual and serious photographers will find it a good deal.
Why We Picked It
Sometimes every cent counts. Canon’s CanoScan LiDE 300 has only half the resolution and is a tad slower than the LiDE 400 that earned a PCMag Editors’ Choice award, and it lacks that flatbed’s ability to stand upright to save desk space. But it costs $20 less and combines good photo scanning quality with a helpful software bundle. Its adjustable lid accommodates thicker items such as books and magazines as well as prints, too.
Who It’s For
We wouldn’t call the CanoScan LiDE 300 better than its model 400 sibling or Epson’s abovementioned Perfection V39, but it does a great job of digitizing photos on a budget. Its Fading Correction and Auto Dust & Scratch Reduction filters pep up aging prints, and it’s a solid choice for dorm rooms and other low-volume scanning environments.
Why We Picked It
As flatbed scanners go, the Epson Expression 12000XL-PH is decidedly expensive, not to mention bulky and heavy. But professional photographers will appreciate this wide-format, high-resolution device’s double-size scanning area (it handles pages up to 12.2 by 17.2 inches) and bundled transparency unit for scanning slides, transparencies, and negatives. Too big for a desktop, it’s designed to take bench space next to your wide-format professional photo printer, where it pairs 2,400-by-4,800dpi resolution with Epson’s ColorTrue II imaging system. The latter combines MatrixCCD scan technology with a zero-warmup-time LED light source.
Who It’s For
Imaging pros will revel in this unit. Thanks to LaserSoft Imaging’s SilverFast Ai IT8 software, the 12000XL-PH provides deep, versatile controls for setting up and enhancing the quality of your scans. Whether you’re scanning slides, 35mm negatives, or oversize artwork and photos, its terrific results make the Epson a prize addition to a professional design or photography studio.
Why We Picked It
The Epson FastFoto FF-680W’s automatic feeder can scan up to 36 prints in one gulp. The ClearClick QuickConvert 2.0 scans only one photo at a time, but on the other hand it’s portable and can scan slides and negatives as well as prints. This four-pound, battery-powered gadget has a 2.4-inch color LCD for viewing images or making setup changes, and it comes with an assortment of bases and holders for everything from 4-by-6-inch snapshot prints to 126 or 110 negatives. It offers resolution up to 14 megapixels, and it supports Wi-Fi as well as USB connectivity. (Plus. you can scan to SD memory cards.)
Who It’s For
Casual archivists will be satisfied with this model, though the ClearClick requires a little patience to use. It’s more for casual or everyday snapshots than prized wedding or prom photos. But it’s a simple device for consumers who don’t need professional imaging quality. For short stacks rather than shoeboxes or scrapbooks full of pics, it does the trick.
Why We Picked It
Plustek’s ePhoto Z300 is a sheetfed rather than flatbed photo scanner, which makes it snappy at processing a stack of prints even though, with no automatic document feeder (ADF), you must insert items one at a time. It’s also affordable—one-third the price of Epson’s FastFoto FF-680W—and scans up to letter-size prints, with a gentle feed mechanism that won’t damage old or fragile photos. Its document-scanning abilities are limited, and its photo quality is no match for fancier scanners, but it’s fine for household tasks such as archiving snapshots.
Who It’s For
If you have a bunch of family photos that need digital preservation, and you don’t have the patience to place them one or two at a time on a flatbed scanner, the ePhoto Z300 and its bundled software are a handy solution. The Epson FastFoto offers both higher volume and better quality, but it costs a lot more.
One feature shared by most “true” photo scanners is a flatbed design. In these scanner designs, you lift a lid to expose a glass platen, onto which you place the image to be scanned. A key differentiator between models is the platen size; most are letter-size (8.5 by 11 inches) or legal-size (8.5 by 14 inches). You’ll want to be cognizant of that spec depending on the size of what originals you will tend to scan.
We strongly urge you to avoid making a habit of scanning photos (or any delicate originals, for that matter) through a sheetfed document scanner, whether it’s a standalone model or one built into a multifunction or all-in-one printer (more on that in a moment). This works in a pinch, but it risks damaging your originals, even if they’re enclosed in a protective sleeve. A notable exception is the Epson FastFoto line of sheetfed photo printers, the latest of which is the FastFoto FF-680W. The FastFoto models are built specifically for scanning shoeboxes full of photo prints and are engineered to treat the prints gently.
Photo scanners are available in a wide range of prices and capabilities. As a rule, low-cost photo models are limited to scanning photo prints. Many do a very good job of this, generally for less than $100. Starting in the $200 range are scanners that can handle slides and/or film negatives. Such models come with plastic frames or holders that fit multiple slides or negatives and secure them in place on the platen during scanning. This helps with alignment and spacing during the scan and reduces the need to handle the transparencies directly off the platen.
Most scanners offer basic and advanced modes, accessible through their driver or scanning software. Typically, a scanner utility’s basic mode will select the scan settings for you, while the advanced mode will let you customize the settings.
Many scanner drivers also have dust- and scratch-removal capabilities. The dust feature sometimes works well, but effective scratch removal really requires a hardware-based solution. To pinpoint this, look for something called Digital ICE technology, which is generally built into some higher-end photo scanners.
Though most people who scan a lot of photos will want a single-function scanner, nearly all multifunction printers (MFPs), also known as all-in-ones (AIOs), include a flatbed that can scan photo prints and other material. The quality of their photo scanning varies widely, depending on the hardware and software involved. Most fall well short of single-function photo scanners in terms of scan quality and/or photo-centric features. (See our picks for the best AIO printers.)
A few models, which we call home photo labs, can scan slides and negatives in addition to prints, providing a variety of photo-centric features as well as the normal MFP functions (printing, copying, and—in some cases—faxing). They’re worth considering if you’re a scrapbooker or a photo buff in need of a versatile photo scanner that can print and make copies, as well. Look at a printer vendor’s specifically photo-minded AIOs to identify these models. There’s no denying that an AIO can save a lot of desk space versus a separate printer and flatbed scanner.
The models we highlight here run the gamut from inexpensive consumer units to scanners aimed at serious photographers, be they professionals or advanced amateurs. What all these products have in common is that they’re the best photo scanners in their respective categories on the market today. The models we discussed above are detailed in our spec table below.
For more on what to look for in a scanner, check out our top overall scanner picks. Also, before you start that big digitizing project, you’ll want to check out our top tips for preserving your photos.
PCMag is obsessed with culture and tech, offering smart, spirited coverage of the products and innovations that shape our connected lives and the digital trends that keep us talking.
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Business Process Management Market Growth Factors with Key Players Software, Appian, Pegasystems – Sioux City Catholic Globe – Sioux City Catholic Globe
Latest business intelligence report released on Global Business Process Management Market, covers different industry elements and growth inclinations that helps in predicting market forecast. The report allows complete assessment of current and future scenario scaling top to bottom investigation about the market size, % share of key and emerging segment, major development, and technological advancements. Also, the statistical survey elaborates detailed commentary on changing market dynamics that includes market growth drivers, roadblocks and challenges, future opportunities, and influencing trends to better understand Business Process Management market outlook.
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List of Key Players Profiled in the study includes market overview, business strategies, financials, Development activities, Market Share and SWOT analysis:
IBM (United States), Nintex (United States) , Automation Anywhere (United States), UiPath (United States), Software AG (Germany) , Appian (United States), Pegasystems (United States), Innovation Group (United Kingdom), Oracle (United States), Genpact (United States), Newgen Software (India) , Bizagi (United Kingdom) , OpenText (Canada) , AgilePoint (United States), ProcessMaker (United States), TIBCO (United States) , Creatio (United States), Bonitasoft (France).
Brief Overview on Business Process Management:
Business Process Management (BPM) is a discipline that involves a combination of process modeling, automation, execution, control, measurement and optimization of the business process flows in alignment with the enterprise goals. This is widely adopted by organization for improving the entire business operations. It empowers organization by managing workflow more efficiently, effectively and make more adaptable to environmental change.
Market Growth Drivers:
Increasing Focus on Automating Businesses
Optimized Resource Utilization Through Automated Business Processes
Automated It Systems Help Serve Customers’ Dynamic Requirements
Key Market Trends:
Streamlined Communication Across Varied Business Functions in the Organization
The Rising Demand for the Cloud Applications
Opportunities:
Integration of AI and Ml Technologies
Rising Demand for Robust Solutions to Maximize the Visibility and Control Over Processes
Challenges:
Growing Cultural Barrier to Adopt Advanced Solutions Over Traditional Systems
Selecting Appropriate Processes to Implement Automation
Segmentation of the Global Business Process Management Market:
by Type (Automation, Process Modelling, Content & Document Management, Monitoring & Optimization), Application (BFSI, IT & Telecom, Retail, Manufacturing, Healthcare, Government & Defense), Deployment Type (Cloud, On Premise), Industry Verticals (Banking, Financial Services, and Insurance, Information Technology, Healthcare and Life Sciences, Retail and Consumer Goods, Telecommunication, Manufacturing, Other Industries), Business Function (Procurement and Supply Chain Management, Sales & Marketing, Human Resource Management, Sales & Marketing, Accounting & Finance, Customer Service Support, Others), Component (Solution (Process Improvement, Automation, Integration, Monitoring & Optimization, and Content & Document Management), Services (Implementation. Consulting and Training & Education)), Organisation Size (Small and Medium-Size Enterprise, Large Enterprise)
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Geographically, the following regions together with the listed national/local markets are fully investigated:
• APAC (Japan, China, South Korea, Australia, India, and Rest of APAC; Rest of APAC is further segmented into Malaysia, Singapore, Indonesia, Thailand, New Zealand, Vietnam, and Sri Lanka)
• Europe (Germany, UK, France, Spain, Italy, Russia, Rest of Europe; Rest of Europe is further segmented into Belgium, Denmark, Austria, Norway, Sweden, The Netherlands, Poland, Czech Republic, Slovakia, Hungary, and Romania)
• North America (U.S., Canada, and Mexico)
• South America (Brazil, Chile, Argentina, Rest of South America)
• MEA (Saudi Arabia, UAE, South Africa)
Furthermore, the years considered for the study are as follows:
Historical data – 2016-2021
The base year for estimation – 2021
Estimated Year – 2022
Forecast period** – 2022 to 2027 [** unless otherwise stated]
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Summarized Extracts from TOC of Global Business Process Management Market Study
Chapter 1: Exclusive Summary of the Business Process Management market
Chapter 2: Objective of Study and Research Scope the Business Process Management market
Chapter 3: Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis
Chapter 4: Market Segmentation by Type, End User and Region/Country 2016-2027
Chapter 5: Decision Framework
Chapter 6: Market Dynamics- Drivers, Trends and Challenges
Chapter 7: Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 8: Appendix, Methodology and Data Source
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Understanding the 3 types of CRM systems – TechTarget
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The right customer relationship management software can help organizations communicate with customers and foster a healthy, long-lasting relationship.
However, before purchasing CRM software, an organization must understand the differences between the three main types — operational, analytical and collaborative — and how to determine which is the right choice.
A CRM system manages and analyzes customer data and interactions to improve customer service, aid in retention and drive sales growth. Over time, the number of tools and functions of a CRM system has expanded to cover every period in the customer lifecycle. But how does an organization know which of the three types of CRM system is right for it?
The three types of CRM systems include the following:
An operational CRM system is the most common type and covers the broadest range of functions. It blends all business processes, such as customer service, marketing and sales, into one within a company. Its primary goal is to help streamline business operations, centralize customer interactions and sales, marketing processes, and service and support efforts in one place. Further, it stores information on customers, leads and employees using a shared platform to better support existing and potential customers.
An operational CRM system captures customer details and uses them to provide service across the customer lifecycle, including marketing, sales and service automation.
An operational CRM can improve organizational efficiency leading to improved processes, internal cohesion and overall customer satisfaction. However, as with each type of CRM, it is dependent on proper data entry to achieve its full effect, which can be a time-consuming process — especially for small or mid-sized organizations.
Operational CRM systems are ideal for helping teams work better to understand customers through the capture and nurture of customer relationships and improve the perception of customers and sales for the company.
The primary goal of analytical CRM is to leverage customer data into trends and actionable insights the organization can use to improve the customer experience. Data from an analytical CRM can include customer preferences, channels, touchpoints, trends, forecasting and more.
Analytical CRM systems excel at helping organizations parse large amounts of data and develop strategies to develop new leads, increase customer retention and create efficiencies for the sales and marketing departments.
However, while analytical CRM systems can help understand a large amount of data, they can also be more technical and difficult to understand without dedicated staff. Additionally, the insights gleaned from the data are only as good as the data entered, and inconsistent entries can skew the results.
Analytical CRM systems are best for mid-size and large organizations that gather large amounts of data and want to distill that data into trends and insights.
Many analytical CRM systems exist, but two standout options include the following:
A collaborative CRM, also known as strategic CRM, enables different teams or functional areas in an organization to share customer data. The result is that everyone uses the same knowledge base about the customers to make decisions, creating a holistic approach to managing customer relationships. While operational CRM tools often focus on marketing and sales, collaborative CRM systems focus primarily on customer service.
A collaborative CRM system has two components: interaction and channel management.
This type of CRM system strives to improve the information shared between the sales, marketing and customer support departments. Because these teams work separately, a collaborative CRM tool can help break down those silos to address customer needs and manage customer relationships effectively.
However, given this system’s interdependent, collaborative nature, it’s essential to use it correctly. Otherwise, the connection between touchpoints could break down, and the customer experience will suffer.
Organizations that operate across multiple locations or work in siloed teams can use a collaborative CRM system to streamline how they gather customer information and communicate with them.
Top 4 CRM systems comparison
Part of: Guide to choosing a CRM system
Sales and marketing terms are like alphabet soup, and the different acronyms can get confusing. Find out the difference between three major platforms: CRM, CDP and DMP.
CRM platforms can boost CX with business insights if sales agents use them properly. Sales leaders should know the benefits and challenges of these platforms before purchasing.
To get the most out of a CRM system, organizations must decide whether an operational, analytical or collaborative CRM system is right for them.
Choosing the right CRM software can be daunting for organizations. Follow these five steps and best practices to ensure the search, adoption and deployment processes go smoothly.
Microsoft’s announcement of Loop came with various questions — in particular, how the new product compares to legacy products, …
Cloud document management company Box chases customers with remote and hybrid workforces with its new Canvas offering and …
Microsoft Syntex is a bundle of AI services for enterprise content repositories. It signals new attention devoted to SharePoint, …
Weighing employee productivity monitoring against remote workers’ privacy is a serious issue that requires protecting personal …
Enterprise collaboration is an integral part of doing business. But companies must learn to guard against voice security issues …
Microsoft plans to release the Teams Premium add-on in February but won’t launch advanced AI capabilities until later in the year…
Data clean rooms are moving into the mainstream as privacy concerns complicate measuring ad ROI, but the ‘people problem’ poses …
Open source-based streaming database vendor looks to expand into the cloud with a database-as-a-service platform written in the …
The tech giant previewed the next major milestone for its namesake database at the CloudWorld conference, providing users with …
CarperAI, Scale AI and other research groups have partnered to deliver instruction-tuned language models as the NLP market grows.
The CRM and business process management vendor, looking to scale up, introduced new features for business users, service …
Nvidia GPU chips will support Oracle Cloud Infrastructure. Also, OCI users will have access to Nvidia’s Enterprise AI system and …
The new Oracle B2B Commerce is aimed at easing the complexity of B2B transactions by integrating finance from JP Morgan and …
Customers at the IFS Unleashed conference showed interest in the IFS ESG module, which is intended to measure ESG data and …
IFS will not try to be the ERP for all companies and purposes, but it will continue to focus on providing functionality for …
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3 Best Practices for File Naming Conventions and Organization – The Motley Fool
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by DP Taylor | Updated Aug. 5, 2022 – First published on May 18, 2022
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We’ve been all there: You need to find a file with critical information on it, and you have no idea where it is. You look through folder after folder. Nothing. You try a different computer. Still no luck. Where the heck is that file?
Unfortunately, if you don’t know what the name of the file is, you’ll be looking for a needle in a haystack. It’s probably named something like 2jfepzbmq238.doc. Only sheer blind luck and hours of digging through files are going to save you now.
As an organization, you don’t want that. Not only does that lead to critical files going missing, but even when your staff is successful in finding the file, cumulatively they’ve wasted hours of work time in the search. And that makes everyone less productive as a whole.
It does no good to have a document management system if you don’t also have a file management system. Here’s what you must know about file naming conventions and how to create your own.
A naming convention for files is a stipulation that files be named in a specific way when doing document archiving in order to keep them organized and findable in your database. A file naming convention ensures that the document is not only easy to find but that a person can also identify the contents of that file by simply reading the file name.
It also explains the relationship of different files to each other. Organizations must have a file naming system in place to avoid lost files and lower staff efficiency.
So why should you have a file naming convention at your business? No matter how small your company is, you probably have a lot of files to juggle. Using file naming best practices in your electronic filing system has three main benefits.
When you have a clutter of files in a folder with no naming convention, it’s impossible to easily identify what files you need access to, resulting in the user opening a multitude of files. This is a tremendous waste of time and can be rectified with a naming convention. You may include the author’s name, the project name, the date, the version of the file, or other info in the file name depending on how you want to organize your files.
Without a naming convention, the search function in your database has limited use. But if you know how files are named, you can quickly bring up the file by searching for a name, a date, or even a file type.
Without a naming convention, your best bet for finding the right file is to use the computer on which the file was originally created. But by having a naming convention in place, it won’t matter whether you’re on a desktop computer, a tablet, or your smartphone — you can bring it up instantly by looking for a specific file name.
So what exactly is the best way to name files? It varies depending on what industry you’re in and how you want to organize your files in your records management system. In general, however, you should take the following steps when naming your files.
Starting your file name with the date the file was created is an easy way to sort your files. For example, you may use “20210115” at the beginning of the file. This indicates the year (2021), month (01 or January), and the day (15th).
Then, you can simply sort by name to organize the files by the date of creation. Starting with the year is best because then you don’t have a situation where multiple files created on Jan. 15 but in different years appear at the top of your results.
Another good identifier to put in the file name is the project description. For example, you could use “MarketingPresentation” to clearly identify it as marketing materials and differentiate it from, say, a sales presentation created on the same date.
If there are multiple versions of the marketing presentation created on the same day and you want to track all of them, add something like “V2” (version two) to the file name.
If you want to make the file name even more specific, use the author’s name in the file. Generally you should limit it to a last name so the file name doesn’t get too long, but use your best judgment — whatever works best for your business.
Once all these elements are identified, create the file name and separate each element with a hyphen or an underscore. So the final file name for the above example could be: “20210115_MarketingPresentation_V2_Smith.pdf.”
Do you have to go with the above example? Of course not! The naming system is up to you. After all, you’ll be the one who uses it. If you want to modify the above naming convention, observe the following three best practices.
Naming conventions must be consistent. If you’re constantly changing them up, it may be better than nothing — they should at least be searchable — but you will still be dealing with an unorganized mess. By staying consistent, your team will know exactly what to look for and all the files will be neatly organized when you sort them. Train staff to use your naming convention to the letter.
Whatever naming convention you go with, it should be descriptive. Even if you’re not concerned with organization, you and your team will save a lot of time by understanding what is in a file without having to open it. Don’t worry too much about an arbitrary length for your file name — it shouldn’t be ridiculously long, but being descriptive is more important than hitting a certain character length.
Generally you want to identify each version of a file and keep the old versions around. Sometimes you may want to go back and look at previous versions of a document, and it costs nothing to keep the old files around. This helps you properly track progress on a project and ensure you are using the latest version available.
People don’t think much about document control until when they need it. But instead of being reactive, be proactive and intelligent about going paperless. Set aside time this week to create document naming conventions and develop a new system for document control at your organization.
Then, explore document management software. These applications will help you not only with naming your files but also with all of the elements of carefully managing your files.
They’ll keep track of a document’s history, showing who accessed files and when. They’ll improve the security of your files to keep them safe from hackers and other unauthorized users. And they’ll make you more efficient, freeing up staff time for other tasks.
Cash back, travel rewards, 0% intro APR financing: all of these can be great credit card perks for business owners. But how do you find the right business credit card for you? There are tons of offers on the market today, and sifting through them to find the right one can be a big hassle. So we've done the hard work for you.
Get started with one of our top business credit card picks of 2022 today.
DP Taylor is a business software expert writing for The Ascent and The Motley Fool.
We’re firm believers in the Golden Rule, which is why editorial opinions are ours alone and have not been previously reviewed, approved, or endorsed by included advertisers. The Ascent does not cover all offers on the market. Editorial content from The Ascent is separate from The Motley Fool editorial content and is created by a different analyst team.
The Ascent is a Motley Fool service that rates and reviews essential products for your everyday money matters.
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Security Compliance Software Market Growth, Size, Share, Trends, COVID-19 Impact Analysis, and Forecasts to 2030 – Sioux City Catholic Globe – Sioux City Catholic Globe
According to the Regional Research Reports, the Global Security Compliance Software Market size was estimated to grow at a CAGR of 10.8% during the forecast period. Security compliance software helps companies document their compliance with cyber security frameworks in order to pass security audits. These tools enable information security or compliance teams to evaluate and manage their security processes. This helps ensure they comply with internal controls and industry or regulatory security frameworks such as SOC2, PCI DSS, ISO 27001, ISO 27002, FedRAMP, NIST 800-171, NIST 800-53, and NIST Cybersecurity Framework, among others. These tools enable security and compliance analysts to assess company systems and policies and document or identify areas of compliance and noncompliance.
Undergoing a security audit can be both a time-consuming and an internal resource-intensive endeavor. This software can ensure gathering compliance information is collaborative, correct, complete, and in the format required by auditors.
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The report analyzes the current trends and future estimations of the global Security Compliance Software market. The report also includes the drivers, restraints, opportunities, and challenges faced by the vendors operating in this market. In addition, the report analyzes the impact of these factors on the growth of the market over the forecast period. To evaluate the market size, the global market research report considers the revenue generated from the sales of Security Compliance Software products. In addition, the market breakdown data for each component, deployment model, organization size, industry, and regional market is provided in the report.
The Security Compliance Software market report thoroughly analyzes detailed macro-micro economic factors as well as segmental and regional market attractiveness. The report will include an in-depth quantitative and qualitative assessment of the segmental/regional market outlook considering the market players’ presence in the respective region/country and segment. The finalized version of the research report also includes the conclusion and recommendation section specific to the buyer of the report.
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Intelligent Document Processing (IDP) Market 2022-2028 | Global Opportunities, Challenges, Drivers, Trends, Key Players, Regional Segmentation, Types, Applications, Developments, Business Strategies – GlobeNewswire
August 30, 2022 02:05 ET | Source: Absolute Reports Pvt Ltd Absolute Reports Pvt Ltd
Pune, INDIA
Pune, Aug. 30, 2022 (GLOBE NEWSWIRE) — Intelligent Document Processing (IDP), a form of Intelligent Automation, is a new system of data recognition and extraction from complex documents. IDP systems use traditional document scanning technology, primarily OCR software, and other machine learning tools to scan, categorize, extract, and analyze data from semi-structured or unstructured documents. IDP systems allow users to seamlessly integrate the data into workflow automations.
Intelligent Document Processing (IDP) market research report is an expert’s analysis that mainly includes companies, types, applications, regions, countries, etc. Also, the reports give analysis on sales, revenue, trade, competition, investment, forecast. Intelligent Document Processing (IDP) market research covers COVID-19 impacts on the upstream, midstream and downstream industries. Also, this study offers detailed market estimates by emphasizing statistics on several aspects covers market dynamics like drivers, barriers, opportunities, threats, and industry news & trends.
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Market split by Type, can be divided into: –
Market split by Application, can be divided into: –
Market segment by Region/Country including: –
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Leading players of Intelligent Document Processing (IDP) including: –
Key Developments in the Intelligent Document Processing (IDP) Market: –
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Detailed TOC of Global and United States Intelligent Document Processing (IDP) Market Report & Forecast 2022-2028
1 Study Coverage
2 Intelligent Document Processing (IDP) by Type
3 Intelligent Document Processing (IDP) by Application
4 Global Intelligent Document Processing (IDP) Competitor Landscape by Company
5 Global Intelligent Document Processing (IDP) Market Size by Region
6 Segment in Region Level & Country Level
7 Company Profiles
8 Research Findings and Conclusion
9 Appendix
9.1 Research Methodology
9.1.1 Methodology/Research Approach
9.1.2 Data Source
9.2 Author Details
9.3 Disclaimer
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Absolute Reports is an upscale platform to help key personnel in the business world in strategizing and taking visionary decisions based on facts and figures derived from in depth market research. We are one of the top report resellers in the market, dedicated towards bringing you an ingenious concoction of data parameters.
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What is open source software? – Área corporativa Banco Santander
What is open source software? Área corporativa Banco Santander
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Marketing Resource Management Software Market 2022 – Top Industry Players as Bynder, Oracle, Adgistics, SAP – Sioux City Catholic Globe – Sioux City Catholic Globe
The study on Global Marketing Resource Management Software Market focuses on the analysis of the present trends in the global market. The objective of MarketandResearch.biz is to provide a comprehensive view of the market to the clients and help them build growth strategies. The Marketing Resource Management Software provides a forecast for a period of 2022-2028 based on an in-depth and expert observation.
The document provides a detailed evaluation of main market players in the global Marketing Resource Management Software marketplace, consisting of product offerings, business overviews, nearby presence, business strategies, mergers & acquisitions, SWOT analysis, latest developments, and significant financial information.
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The study has thoroughly examined crucial factors which include drivers and restraints, opportunities, production, market players, and competition. Important industry trends, market size, market proportion estimates, and profiles of the top industry competitors are all discussed on this document.
The income projection is based absolutely upon the modern performance of the phase.Report emphasis on assessment of nearby phase:
Report offers a granular evaluation of the market phase of the marketplace:
The segmentation analysis includes a descriptive assessment of the segments, presentation of the market stocks constituted by each section, increase rate of every section, and attractiveness of the segment in terms of revenue.
The producers worried in the marketplace include
Report offers a granular evaluation of the market phase of the market:
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The report delves first into the foundation of the Marketing Resource Management Software by looking at definitions, categories, and market overview. This then further helps understand the various product specifications, supply chain, manufacturing process, and the cost structure and thus provide a depth in the understanding of the building blocks of this industry and understand the major drivers of change in it.
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Evidence of co-creation practices in suicide prevention in government policy: a directed and summative content analysis – BMC Public Health – BMC Public Health
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BMC Public Health volume 22, Article number: 1929 (2022)
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In Australia, the collaborative involvement of stakeholders, especially those with lived experience in mental health and suicide prevention, has become important to government policy and practice at Federal and State levels. However, little is known about how governments translate this intention into frameworks of co-creation for policy, funding programs, service improvement, and research and evaluation. We investigated the extent to which publicly available government policies refer to collaborative practice using an established translation model.
An exploratory directed and summative content analysis approach was used to analyse the contents of Federal (also known as Commonwealth), State and Territories policy documents on mental health and suicide prevention published in Australia between 2010 and 2021. The data was extracted, compared to an existing translation model, and summated to demonstrate the evidence of co-creation-related concepts between government and stakeholders.
40 policy documents (nine at the Federal and 31 at the State and Territory level) were identified and included in the analysis. Only 63% of policy documents contained references to the concept of co-design. Six of the State policies contained references to the concept of co-production. Across all policy documents, there were no references to other concepts in the model adopted for this study, such as co-creation, co-ideation, co-implementation, and co-evaluation.
Although the government at Federal, State and Territory levels appear to support collaborative practice through partnership and co-design, this study suggests a narrow approach to the theoretical model for co-creation at a policy level. Implications for both research and practice are discussed.
Peer Review reports
In Australia, mental ill health and suicide cost the community between $43 billion and $70 billion annually [1, 2], while the estimated national expenditure on mental health services in 2018–2019 amounted to $10.6 billion [3]. Meanwhile, annual deaths from suicide in Australia stand at 12.1 per 100,000 people [4], with rates of attempted suicide and suicidal ideation on the rise [5]. Suicide and suicidal behaviour remain significant economic and epidemiological burdens in Australia, leading researchers, organizations, and government agencies to seek innovative approaches and practical solutions when addressing these ongoing mental health and suicide issues. One creative strategy involves governments and researchers engaging with consumers and carers to develop mental health policy and improve how services are delivered. The promotion of user involvement or a person-centered approach comes from the “nothing about us without us” [6] and the “sit beside me, not above me” [7], both of which promote greater carer and consumer involvement in the decision-making process. Alongside the increased participation of users in the planning and delivery of mental health and suicide prevention services, multisectoral collaborations between government, researchers, service providers, and users are also becoming increasingly significant. It is argued that multisectoral collaborations may resolve complex issues such as suicide prevention more effectively than researchers alone [8]. For instance, translation frameworks such as co-creation of new knowledge are a current example of how stakeholders (researchers and other stakeholders, including those with lived experience) may collaboratively engage in program evaluation through four collaborative processes, that is, i) generating an idea (co-ideation); ii) designing the program or policy and the research methods (co-design); iii) implementing the program or policy according to the agreed research methods (co-implementation), and iv) the collection, analysis and interpretation of data (co-evaluation)” [9]. For governments, several benefits come from increased stakeholder participation in research and service delivery. For instance, engaging stakeholders in the design phase may result in mental health and suicide prevention services meeting the needs of stakeholders [10].
Additionally, involving stakeholders in the research process will increase stakeholders’ participation, particularly if the research impacts policies that directly affect them [11]. Collaborations between researchers and consumers may also improve service quality and outcome effectiveness by evaluating suicide prevention programs. In turn, this can enhance the development of sustainable research and innovation [12, 13]. Despite such promises, it is unclear whether mental health and suicide prevention policies reflect ideas on person-centered participation and multisectoral collaboration. Remarkably, there is little clarity about whether collaboration between stakeholders impacts research outcomes and funding and how it is measured. Optimising the impact of collaboration is critical, given that policy and funding remain primary drivers in the development of mental health and suicide prevention strategy and the services delivered through community organisations [14]. Identifying any gaps in policies that may limit the implementation of effective collaborative practices will improve understanding of how the existing policies and their scope for action are likely to be used.
A complete examination of Federal, State and Territory policy documents is needed to understand how collaborative processes involving stakeholders, especially those with lived experience, are represented through mental health and suicide prevention policies. The study will reference an existing peer-reviewed theoretical framework, “co-creation of new knowledge,“ to compare critical elements associated with collaborative processes within the research cycle [9]. Our knowledge indicates that no published research has previously examined mental health and suicide prevention policies in co-creation or collaborative processes.
The following aims were formulated in conjunction with the authors, who identify themselves as either a researcher, service provider, a person with lived experience, or a combination of roles. Specifically, the study has four aims: (1) identify mental health and suicide prevention policies published between 2010 and 2021; (2) capture the frequency of keywords and compare them across identified policy documents (3) describe links between mental health and suicide prevention funding and the principles of co-creation and; (4) assess how policies prioritise four co-creation related activities (co-creation of new knowledge), and other collaborative activities. This study addresses the critical debate on the gap.
The research questions were addressed using content analysis methodology. By definition, content analysis is a qualitative descriptive methodology used to make “valid inferences from verbal, visual or written data in order to describe and quantify specific phenomenon“ [15] (p.18). It is beneficial for studies where the purpose is not to collect rich descriptions of the findings but to detect patterns or outliers within qualitative data [16].
For this study, we chose a combination of directed and summative content analysis, two well-known approaches used in content analysis. This approach has been commonly used for the research of documents requiring a low level of interpretation [17], such as health guidelines [18] and policy documents [19]. Specifically, a directed content analysis involves using a set of pre-defined codes (deductive coding) created from an existing theory or framework to categorise data [20]. The application of deductive coding increases the likelihood that both manifest content (observable and direct representation of specific words) and latent content (underlying representation and interpretation of concepts) are captured [21]. Meanwhile, for summative content analysis, the frequency of specific words appearing in the text are counted and compared across coding categories, followed by further analysis to interpret the context of the frequency of words [20]. A directed content analysis was used for Aims 2 and 3, while a summative content analysis was used to address Aim 4.
We defined health policies as documents meeting the following three principles: (1) authored by a governing body ( elected to exercise authority), (2) a document that outlines the objectives, strategies, or goals, and, (3) includes the planning, organisation, delivery or improvement of services, programs or strategies [28]. In Australia, policy documents are labelled using a variety of terms, including “strategy”, “policy”, “strategic plan”, “plan”, “strategic framework”, “action plan”, “framework” and, “report”. To identify relevant mental health and suicide prevention policy, we used the following inclusion criteria: (1) satisfy the definition of a policy document where an Australian governing body authors documents at the Federal, State or Territory level, (2) policy documents had to have a primary focus on issues relating to mental health and/or suicide prevention, and (3) policy documents were limited to those published after January 2010. The period of January 2010 to October 2022 was determined following a pilot search of the literature where co-related type terms (e.g., co-design, co-production) began appearing in the academic literature alongside suicide prevention and mental health.
From 1 to 2021 to 15 October 2021, we completed three discrete rounds of searching to identify publicly available and relevant policy documents on mental health and suicide prevention. The first round targeted Australian government health websites at the Federal, State and Territory levels. The second round involved a title and abstract search of Trove, the National Library of Australia (NLA’s) [22] online library database aggregator, along with a grey literature search using Google and Google Scholar. These two searches were optimised by a third manual search of policy documents. This third process involved scanning policy documents retrieved in the first and second rounds of searching for references to additional policies. Keywords used to search the databases and websites included “mental health” and “suicide” with searches limited to those publications published after January 2010 and websites using Australian government domain names “.gov.au”.
Our quality assurance process included checking all identified policies against Mindbank, a database maintained by the World Health Organization [23], which lists health policies by country and specialty, including suicide prevention, and asking three experts to review the final list and identify any missing policies.
All identified documents retrieved from online searches were imported into Endnote X9, where a reference library had been created to allow storage and management of full-text documents. Following this step, the full-text versions of identified documents were then exported to NVivo 12 Pro QSR, a qualitative analysis software.
For the deductive analysis, we chose to analyse manifest and latent representations of terms relating to the co-creation of new knowledge framework. As briefly described in the introduction, co-creation of new knowledge is a translation model which works alongside the delivery of health interventions such as suicide prevention programs [9] and relies on the collaboration between researchers, third sector organisations, and those with lived experience to generate new knowledge. Through this process, stakeholders engage in five collaborative processes these being co-creation, co-ideation, co-design, co-implementation, and co-evaluation. Since the aim was to identify the usage of these “co” processes within policy documents, the co-creation of new knowledge framework, as presented in Table 1, guided the makeup of categories used in NVivo 12 Pro QSR.
To expedite the data analysis process, we used the text search function in NVivo 12 Pro QSR to search policy documents for the five co-related processes.
We used a manual open coding process for the inductive analysis to identify the five co-creation-related domains. This process involved the lead author becoming familiar with the data through careful reading and re-reading of the documents and manually coding text relating to the co-creation process by highlighting manifest or latent phrases or segments of data. During the manual coding process, the data was categorised into themes and subthemes, which, over time, were reviewed and refined to represent ideas and patterns of meaning. Emerging themes and sub-themes were discussed with authors KM and SW and were further refined through this discussion. Data on the coverage or frequency of terms was collated using NVivo 12 Pro QSR and converted into a heat map using Microsoft Excel. The coverage of terms indicates how often categories of co-creation and related concepts were cited across mental health and suicide prevention policy documents. Coverage data provides insight into the significance of specific terms. Therefore, the higher the level of coverage of a concept or term, the higher the rate of interest in or discussion of that term across the documents. For terms appearing in multiple policy documents published in the same year, the average of the coverage rate was reported. In addition to the coding process and the coverage data, a data extraction form was developed in Excel to capture critical information on policy characteristics, including the name of the policy, year, level of government (Federal, State or Territory), and policy focus (mental health or suicide prevention).
Trustworthiness.
The trustworthiness of the content analysis was evaluated using Lincoln and Guba’s [24] four standards (credibility, dependability, transferability, and confirmability) evaluation criteria. Credibility was achieved by including sufficient detail about the data analysis process and using a process of systematically comparing categories to ensure consistency of the data had been maintained. Dependability was demonstrated by maintaining clear documentation about the process used to collect data, and the development of the coding frame was reviewed by three of the co-researchers. Meanwhile, transferability was reached by ensuring all relevant Federal, State or Territory policy documents on mental health and suicide prevention were included. At the same time, the data was confirmed through feedback from several co-authors, all of whom are experts in mental health and suicide prevention. In addition, confirmability was further attained through an audit trail whereby tables and results demonstrate transparency of the data collection and analysis.
We searched the literature and identified 40 unique mental health and suicide policy documents meeting the study inclusion criteria. Nine related to Federal policies [2, 8, 25,26,27,28,29,30,31] while the remaining 31 documents represented the following Australian States and Territories: New South Wales (NSW) (n = 8) [32,33,34,35,36,37,38,39]; Northern Territory (NT) (n = 4) [40,41,42,43]; Queensland (QLD) (n = 4) [44,45,46,47]; Western Australia (WA) (n = 4) [48,49,50,51]; South Australia (SA) (n = 4) [52,53,54,55]; Tasmania (TAS) (n = 4) [56,57,58,59]; Victoria (VIC) (n = 2) [60, 61], and Australian Capital Territory (ACT) (n = 1) [62]. The field of mental health was the focus of one Federal policy [27] and 12 State policy documents [32, 37, 39,40,41, 44, 46, 48, 52, 53, 58, 60], while four Federal [8, 25, 26, 31] and 17 State and Territory policy documents [33,34,35,36, 38, 42, 43, 45, 47, 49,50,51, 54,55,56,57, 61] were solely dedicated to suicide prevention. The remaining six policy documents (Federal n = 5; Territory n = 1) covered mental health and suicide prevention [2, 27,28,29,30, 62].
Table 2 provides the results of the deductive and inductive analysis, including the number of references for each term and exemplar quotes to demonstrate the results presented. Of the group of terms relating to the co-creation framework listed in Tables 1, only “co-design” was cited. Meanwhile, domains identified through inductive coding generated an additional six categories of terms frequently used in conjunction with co-creation of new knowledge. These included “collaboration”, “funding”, “research and evaluation”, “stakeholders (including lived experience)”, “Third Sector Organisations” and “co-production”. The most frequently cited terms were “collaboration” (n = 637) and “funding’ (n = 628). The next most frequently cited terms were “stakeholders” (including Lived Experience)” (n = 408), “research and evaluation” (n = 350) and, “Third Sector Organisations” (n = 236). Co-production was cited the least across all policy documents.
Across 25 Australian policies, six Federal [2, 25, 27, 29,30,31] and 19 State and Territory documents [32, 33, 35,36,37,38,39, 41, 45,46,47, 49,50,51, 53, 57, 60,61,62], there were 107 references to the word “co-design” (and its variants including co-designed and co-designing). Overall, nine policies [2, 25, 27, 30, 32, 35,36,37, 60] offered definitional descriptions of co-design, with one NSW policy [32] perceiving the concept as a tool for services where co-design is used: ”to work collaboratively with staff, consumers, families and carers on redesigning mental health services to prevent suicides among people under care” (p.17), and as an approach to assist “services to deliver person-centred care through considering consumer, carer, staff and other stakeholder perspectives in planning and service delivery” (p.129). The same policy also chose to describe co-design in terms of the individuals involved and benefits, for example:
“Co-design…..brings together the expertise of people with a lived experience of a suicide attempt or who have been bereaved by suicide, families and carers, service providers, key stakeholders and community groups to produce an outcome which is mutually valued across the community” [32] (p.21).
Meanwhile, the NSW Aboriginal and Mental Health and Wellbeing Strategy 2020–2025 [37] defined co-design in the context of health services as “a collaborative approach…to improve health services. In co-design, the people who use and deliver health services are deliberately engaged to share experiences and collectively imagine and create solutions that innovate, change and improve health services” (p.13). In analysing co-design, the authors observed no discernible trend in discussions about (i) co-design in connection with policy aims; (ii) how organisations, such as TSOs, might engage in co-design; or (iii) guidance on the potential benefits or challenges of such a collaborative process. According to the documents, co-design is a best practice model for developing tailored mental health and suicide prevention services that meet the needs of individuals and their families. Meanwhile, co-production appeared in six policy documents [32, 49,50,51, 53, 60], wherein co-production was used as a synonym for co-design. For example, an extract from Victoria’s 10-year mental health plan [60] describes co-production as a collaborative process where:
“government will co-produce policy and services with people with mental illness, their families and carers, and clinicians and other mental health workers. People will have a genuine say about how the system works, how services work and how they are treated. The result will be services that work much better for the people they serve” (p6).
The concept of collaboration was the most commonly used co-creation-related term across all 40 policy documents, with 637 manifest or latent references identified. In 12 documents [29, 31, 34,35,36, 39, 41, 42, 52, 56, 60, 62], references to collaboration were made in the broader context of a “whole of government” or a “whole of community” approach. These approaches characterise the forming of strong, co-ordinated partnerships between all sectors of government, and stakeholders, including researchers, TSOs, carers and consumers, to strengthen communities and improve suicide and mental health initiatives. For instance, at a Federal level whole of government approach is seen to “unlock the potential of a whole of government delivery model by ensuring each individual agency has strong processes and accountabilities for delivering agreed suicide prevention initiatives, and linking into broader collaborative efforts across government” [31] (p24). Meanwhile, policies view “a whole of government, whole of community approach” [35] as a formal linking of activities “that places greater emphasis on integration and collaboration between all levels of government, individuals and communities, the non-government and private sector, and people with lived experience” [35] (p2). Across policies, collaboration was generally described in favourable terms espousing the benefits partnerships provide towards improving suicide and mental health outcomes. For instance, “The role of carers and consumers in supporting and informing intersectoral collaboration will be essential at all levels of policy, planning, research, service development and delivery in order to ensure the best possible health outcomes” [52] (p14) and, “growing body of evidence shows that services designed in collaboration with those who use them are more efficient and less expensive” [39] (p47). A strong emphasis was also placed on collaboration and equity by one state policy wherein: “Research shows that giving people an equal voice as active partners in healthcare improvement can lead to better experiences and outcomes for all. A key to improving outcomes is respecting the expertise of consumers, carers and staff in guiding individual recovery as well as co-design” [32] (p82). Latent examples relating to the idea of collaboration between consumers and carers used terms such as consultation and engagement to describe the collaborative process between consumers and carers “Supporting consumers and carers to effectively engage and participate will remain a key focus of the NMHC’s work. This will include consultation and engagement on a range of issues, from an individual accessing mental health services, to the contribution of consumers and carers to mental health service planning, delivery and engagement on mental health reforms” [2] (p9).
The term “stakeholders” includes references to “lived experience” (also known as consumers or peer workers), featured in 34 policy documents with 408 manifest or latent mentions. In all of the documents, the concept of stakeholders extended to include individual groups such as “those impacted by suicide, researchers, non-government service providers and State Government agencies” [47] (p3), with their role defined as working “collaboratively to ensure a comprehensive and coherent approach to legislation, policy, planning, funding and service delivery” [46] (p16). References to stakeholders were significantly focused on establishing “equal partnerships” [46] between stakeholders and mental health consumers. In this context, those with “lived experience” were seen to have “a valuable, unique and legitimate role in suicide prevention” [49] (p10) and an essential factor in creating change through research and practice “we must position lived experience knowledge at the forefront of research, policy and practice. Without it, our reforms and service improvements will fall short of what people need and what they deserve” [8] (p2). In 13 policies [25, 27, 32, 33, 35, 37,38,39, 45, 50, 51, 53, 60], the role of mental health consumers shared a strong connection with co-creation related activities such as “co-designing” programs and services, as evidenced by statements such as “the development and implementation of suicide prevention strategies must include their voices, and activities should be co-designed with people with a lived experience” [50] (p2). There were eight policies [8, 25, 26, 31, 39, 46, 50, 51] referencing the integral participation by Indigenous or cultural groups as stakeholders in the planning of programs and services “Governance must also incorporate early input from the portfolio’s priority populations to ensure approaches are relevant, respectful and effective. This includes cultural governance inclusive of Indigenous people and integrating people with lived experience into planning and advisory stages” [31] (p27) and “The insights of people with lived experience of suicide; traditional forms of knowledge, such as from Aboriginal people and unique cultural perspectives, can form part of the evidence base for effective suicide prevention. Continual development, implementation and evaluation of existing and future initiatives is crucial” [50] (p11). Meanwhile, other examples highlighted the importance of Indigenous involvement in co-design and service delivery but failed to explain how such an approach might work. For instance,
“Aboriginal people are experts in Aboriginal communities and needs, and that improvements in the coordination of services and in the quality of service delivery and planning will need to start in genuine co-design processes, led by Aboriginal people. Person centred and culturally safe services acknowledge the strength and resilience of Aboriginal people, families, and communities” [37] (p10).
The concept of “Third Sector Organisations” and related terms such as “non-government” and “non-profit” appeared in 236 references across 34 policy documents. In one suicide prevention strategy document, TSOs were identified as a type of stakeholder who worked collaboratively with other actors: “Suicide prevention is complex – and it is everyone’s business. A coordinated, well-integrated and compassionate approach is required across all levels of government and from the community, including individuals, families, schools, researchers, community groups, non-government services and the private sector” [35] (p7). While there was evidence of government support for the contribution TSOs make, for instance, “Government also recognises the significant achievements of the non government sector in suicide prevention to date, and acknowledges that building stronger partnerships between government and non government organisations is critical to supporting those at risk of and impacted by suicide” [34] (p1). There was also explicit pressure on TSOs to demonstrate effectiveness and performance measures, where it was suggested, “Tie receipt of ongoing Australian Government funding for government, NGO and privately provided services to demonstrated performance” [2] (p53). Only three policies mentioned TSOs and participation in research [26, 34, 51] with strategies proposing the development of “options for prevention research partnerships between the community sector, non-government organisations and research and training sectors to build capacity in suicide prevention” [26] (p38). Surprisingly, besides a brief mention of TSO participation in a co-design workshop [35], policies contained no explicit or implicit references connecting TSOs and engagement in co-creation-related-activities such as “co-design”.
The terms “research” or “evaluation” appeared in 39 of the 40 policy documents, generating 350 references. Notably, manifest or latent references to co-creation activities such as co-design or co-production were infrequently discussed in close proximity to concepts of research and/or evaluation (n = 14) [25, 27, 30,31,32, 37, 40, 41, 45, 47, 52, 54, 56, 61]. In these cases, co-design (or co-production) was only described in general terms and there were no explicit or implicit references on how co-design could be incorporated into research and evaluation. For instance, when referring to reforming the mental health system, one policy implicitly stated:
“Collaborative partnerships with consumers and carers are integral to successfully implementing changes that improve outcomes for people with, or at risk of, mental illness and/or suicide. Examples of supporting ongoing and active involvement of consumers and carers include collaboration on design and planning, implementation, monitoring and evaluation” [27] (p49),
while a Federal policy referencing a national person-led system asserted, “The system will include capacity building and tools for modelling, need analysis, co-design, implementation and evaluation” [30] (p21).
A search of all included policy documents revealed no discernible evidence of government declarations of support for research and evaluation of co-creation-related activities. However, among the manifest references where co-design was associated with funding, we identified two references: “Funders need to ensure they are supporting the ACT mental health workforce, including they are engaged in co-design of system reform” [62] (p32), and “$1.1 million to the Black Dog Institute to work with the Aboriginal and Torres Strait Islander Lived Experience Centre, supporting the inclusion of people with lived experience in the co-design, implementation and evaluation of suicide prevention activity” [25](p19). Overall, funding-related references were associated with the funding of services [62], how funding was sourced [27], and funding models [53]. In other examples of discussions around funding, there was criticism of how the failure of evaluation and funding leads to poorly planned assessments of interventions. For instance, in a strategic plan published by the Mental Health Commission of NSW [39], it was stated: “While all funded initiatives are required to have an evaluation component, evaluation requirements are not always rigorous enough and funding is not always sufficient for meaningful evaluation, which limits their contribution to the evidence base” (p 37). References emphasising the importance of research and evaluation could be found in a list of Federal standards and quality in suicide prevention for Aboriginal and Torres Strait Islander communities [26], where it was noted that,
“Provision for evaluation can be significantly improved in funding arrangements under state and Commonwealth contracts. There are currently very few evaluations conducted that contribute to the evidence base in any way. Aboriginal and Torres Strait Islander community services benefit from evaluations of programs that demonstrate their effectiveness and that provide information for practice development, policy and planning” (p44).
In the same document, emphasis is put on ensuring that “suicide prevention principles are embedded in systems of quality improvement for social and emotional wellbeing and mental health care” [26] (p44) while failing to include the embedding of rigorous research methods or data collection into service delivery. Meanwhile, of all State policies, the South Australian Suicide Prevention Strategy 2012–2016 [54] was the most explicit in its approach stressing the importance of linking funding with evaluation: “State funded programs to be evaluated prior to funding renewal” and “All suicide prevention programs be properly evaluated with at least 15% of all funding allocated to suicide prevention programs being spent on evaluation.” (p44).
The density of coverage (darker shade represents greater frequency) of terms across Federal, State, and Territories (Table 3) by publication year are depicted as heat maps. Coverage of co-design, for instance, was strongest in 2020, while the use of terms relating to “collaboration” peaked in 2018. In 2012, across both Federal, State and Territory policies, the term “research” attracted the most coverage, while discussions relating to stakeholders were most prominent in 2020. While references to TSOs were highest in 2010, there was a drastic decline in discussions of TSO in suicide prevention, suggesting interest by the government in TSO-led suicide prevention services had waned over the following decade.
This study identifies a gap between publicly espoused policy directions and actual practice. Specifically, we found that the main focus for suicide prevention policy was on co-design and, to a lesser extent, co-production as a form of collaborative practice between stakeholders. The government views these two constructs as the driving factor in the collaborative planning, design, implementation, and evaluation of mental health and suicide prevention projects. The government considers co-design a tool for bringing relevant groups of people together to make the design of programs and services more efficient and effective.
However, no terms relating to co-creation were identified apart from co-design, with co-production being a term that sits outside of the co-creation model. This is an important detail as co-creation of new knowledge represents a translation model which works to ensure investment by stakeholders in the research process. Furthermore, there was no evidence of discussion around the use of robust or rigorous research methods in these collaborative activities. Perhaps this represents an assumption on behalf of the government that rigorous evaluation will be incorporated into practice without it being explicitly stated in policy. However, as evidenced by a report on the evaluation of Indigenous programs, only 6% (3/49) of programs utilised rigorous methods, and of those, none met the criteria of gold standard (Randomised Control Trials (RCTs) [63].
Second, our analysis of references to “third sector organisations or non-government organisations” shows the intent to describe the role of TSOs in mental health and suicide prevention using broad, sweeping statements. Across the 40 documents, there is no substantial evidence of a link between TSOs and the concepts of co-creation. It is clear that TSOs are essential in delivering support services and collaborating with a wide range of stakeholders, including those with lived experience, primary health networks, and government agencies. However, no description of how this collaboration will be managed or how it looks from a practical standpoint is provided. For policymakers, TSOs’ roles were defined in terms of service delivery rather than equitable participation in research. Consumers, carers, and people with lived experience, however, were seen as integral to research and evaluation. With an inherent lack of activity and broad references to collaboration, the inclusion of TSOs could be interpreted as tokenistic.
We uncovered three critical disconnects. First, besides offering definitions and characteristics of co-design, policies offered little guidance on how communities, like TSOs and those with lived experience, might implement co-design into suicide prevention initiatives. Policies presented no monetary encouragement for communities and organisations to engage with collaborative processes like co-design (even though throughout all of the policies, collaboration between carers, consumers or lived experience, TSOs, and other stakeholders, were strongly promoted). Second, there were no explicit or implicit references regarding the role of researchers when collaborating with those with lived experience or TSOs, even though keywords such as research and evaluation were frequently mentioned throughout the included policy documents. Third, the policy research gap remains an ongoing challenge. Although this paper’s findings indicate support for collaborative practice and co-design, a recent systematic review observed no discernible trends relating to multisectoral collaborations or co-creation-related activities, including co-design in suicide prevention interventions [64]. These disconnects in policy implementation arguably impact how effective and appropriate collaboration can be undertaken between researchers and other stakeholders. The benefits of multisectoral collaboration should be considered, given the high emotional, social, and economic costs of suicidal behaviours and the need to ensure that the prevention and intervention services provided can support the communities they claim to target.
In synthesising our findings, there are two key considerations for future policy development should collaborative practices continue to be espoused as important to service development funded through government avenues. First, linking funding to the co-creation activities, specifically by including people with lived experience, TSOs, and researchers throughout the cycle. For this to be fully embedded in policy, funding and reporting must be linked to these activities. Second, inconsistent terminology leads to confusion about the importance of different tasks. The issue of “conceptual ambiguity” around co-related terms makes it “difficult for service providers and policymakers to engage in co-creation activities because they are being asked to engage in a process that either lacks clarity or is highly variable across different researchers and disciplines” [9]. For planning, describing, and evaluating, it is therefore essential that universities and industry, e.g., researchers and TSOs, distinguish between co-creation and co-design.
At the time of writing, the research team is unaware of other published studies examining the presence of co-creation in policies on mental health and suicide prevention. Examining how these practices are, or are not, embedded within the policy sphere is a way of understanding the importance placed on these activities by the main funding bodies of health and human services in Australia. A further strength is using both summative and directed content analysis to collect manifest and latent data as frequency counting of keywords. This approach provided a holistic approach to interpreting the issues specific to mental health and suicide prevention policy documents [65].
Among our limitations were the eligibility criteria and the definitions of policy documents. Most of the documents included in this study represent early-stage policy documents or plans. Therefore, they are not manifestations of policy action or implementation. A further limitation is our sole reliance on policy documents, whereas we could have supplemented our understanding of policy context by introducing alternative perspectives through qualitative interviews with government representatives.
An examination of 40 mental health and suicide prevention government policies over a 10-year period have revealed continuous commitment by the Australian Federal, State and Territory governments to include concepts such as lived experience and co-design in suicide prevention. However, a detailed examination of these policies reveals that lived experience and co-design are oversimplified terms that fail to capture the complexity of implementing and evaluating these programs and what they mean in the context of suicide prevention. The importance of a comprehensive approach to the co-creation of new knowledge is yet to be realised. While the broad policy intent around collaboration is welcomed, if this is not reinforced through policy references to the range of processes and practices surrounding co-creation, there will not necessarily be the depth and range of stakeholder, lived experience, researcher and community involvement required for success. This narrow policy orientation around co-design and co-production may restrict the potential for policy, program, and service improvements in mental health and suicide prevention.
The data used to support the findings of this study are available on request from the corresponding author.
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This research is supported by an Australian Government Research Training Program (RTP) Scholarship.
School of Health, University of New England, 2351, Armidale, NSW, Australia
Tania Pearce, Myfanwy Maple & Sarah Wayland
Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, UK
Kathy McKay
Tavistock and Portman NHS Foundation Trust, London, UK
Kathy McKay
National Drug and Alcohol Research Centre, University of New South Wales, Randwick Campus, 22-32 King Street, 2031, Randwick, NSW, Australia
Anthony Shakeshaft
Centre for Mental Health, School of Population and Global Health, University of Melbourne, VIC, 3010, Melbourne, Australia
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Pearce, T., Maple, M., Wayland, S. et al. Evidence of co-creation practices in suicide prevention in government policy: a directed and summative content analysis. BMC Public Health 22, 1929 (2022). https://doi.org/10.1186/s12889-022-14313-3
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Received: 14 March 2022
Revised: 27 September 2022
Accepted: 30 September 2022
Published: 17 October 2022
DOI: https://doi.org/10.1186/s12889-022-14313-3
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