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[Upbeat jazz music playing. . .]

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Welcome to Part 2 of “Using CLEAR to inform decision making.”

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This video presents two use case examples

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of how CLEAR can be used to help make informed, evidence-based decisions.

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If you have not had the chance to view Part 1,

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which provides an overview of what CLEAR is and the different resources it offers,

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you can do so by visiting the Resources page of the CLEAR website.

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You may be wondering how you can use CLEAR to find relevant studies,

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learn from the findings to improve your programs,

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and make informed decisions that are based on evidence.

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Let’s take a look at an example.

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Meet Teri.

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Teri oversees programs that provide employment and training services

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at American Job Centers, or AJCs.

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These include reemployment programs intended to connect people to services

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that help them find and keep jobs.

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Teri’s state recently received a grant to help connect jobseekers

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to employment opportunities.

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Teri’s state and local AJCs are currently running a number of programs

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to quickly connect jobseekers to employment,

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and Teri wants to make an evidence-informed decision

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about what effective services to include in the new program.

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To help inform her decision, Teri reviews different types of evidence.

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She reviews the AJCs’ performance reports, data,

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and other documents to figure out what services seem to be in demand.

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She also asks her colleagues who work at neighboring states or nearby AJCs

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to see what services they think are potentially promising.

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Importantly, Teri wants the new grant program

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to be informed by the best research evidence out there.

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So, Teri uses CLEAR to find and read up on studies about reemployment programs,

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and to see which programs and practices have shown evidence of being effective,

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especially in settings like hers.

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Teri learns from a CLEAR synthesis report on reemployment services

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that job search assistance services had favorable impacts on individuals’ employment.

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She also finds out that lighter-touch programs, like changing employer contact requirements,

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could potentially speed reemployment.

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Teri uses CLEAR’s searchable database

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to find some of the specific studies listed in the synthesis,

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as well as studies on the kinds of services already being used

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or recommended by her colleagues.

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She reads their short profiles to learn more about where and how

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the programs tested were implemented, and what they found.

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She uses these studies to identify some new program services

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she wants to include in the new reemployment program.

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Teri now has an understanding from CLEAR about the practices that research shows

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have helped connect people to employment.

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She has looked at a variety of evidence and can make an informed decision

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about what effective or promising services to include in the new program.

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She is confident the new funding will help improve the labor market outcomes

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of the people her local AJCs serve.

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That’s just one example of how CLEAR makes research on labor interventions

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actionable to inform decision-making.

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Here’s another example on how CLEAR can help.

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Meet Paulo.

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Paulo works for his state’s Division of Labor Statistics and Research.

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Workplace illnesses and injuries have recently increased in Paulo’s state

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and his division has been tasked with hiring an independent evaluator to

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(1) assess the effectiveness of inspections on workplace safety and

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(2) inform potential improvements to the state’s health and safety inspection strategies.

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Paulo understands the learning priorities of this effort

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and he wants to be sure his state funds a high-quality study.

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Paulo suggests using CLEAR’s Causal Evidence Guidelines

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as a way to inform the new study’s design

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so it can provide them with the kind of evidence the state seeks.

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The guidelines provide the evidence criteria CLEAR uses for rating causal studies;

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in other words, the criteria the studies must meet to provide reliable evidence.

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He walks through the different designs and concludes the state should ask evaluators

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to propose study designs that can produce higher evidence ratings

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according to CLEAR standards, such as randomized controlled trials,

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interrupted time series, and comparison group designs.

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By using CLEAR’s causal evidence guidelines,

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Paulo and his division will be able to launch an evaluation

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that can provide them with more credible, higher quality evidence

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and inform their thinking about how to improve safety in their state.

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As these two examples illustrate, CLEAR is a central,

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accessible resource you can use to support your decision-making.

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For more information on CLEAR, you can visit clear.dol.gov.

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To learn more, check out other videos on our website, review CLEAR’s reference documents,

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or see our FAQ for quick answers to common questions.

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You can also contact us through our website at clear.dol.gov

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if you have a specific question about CLEAR.

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This concludes our two-part video series on “Using CLEAR to inform decision making.”

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You can find links to these videos and other helpful resources about CLEAR

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on the Resources page of the CLEAR website.

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We hope to stay connected through CLEAR in the future!

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