[Upbeat jazz music playing. . .]
Welcome to Part 2 of “Using CLEAR to inform decision making.”
This video presents two use case examples
of how CLEAR can be used to help make informed, evidence-based decisions.
If you have not had the chance to view Part 1,
which provides an overview of what CLEAR is and the different resources it offers,
you can do so by visiting the Resources page of the CLEAR website.
You may be wondering how you can use CLEAR to find relevant studies,
learn from the findings to improve your programs,
and make informed decisions that are based on evidence.
Let’s take a look at an example.
Meet Teri.
Teri oversees programs that provide employment and training services
at American Job Centers, or AJCs.
These include reemployment programs intended to connect people to services
that help them find and keep jobs.
Teri’s state recently received a grant to help connect jobseekers
to employment opportunities.
Teri’s state and local AJCs are currently running a number of programs
to quickly connect jobseekers to employment,
and Teri wants to make an evidence-informed decision
about what effective services to include in the new program.
To help inform her decision, Teri reviews different types of evidence.
She reviews the AJCs’ performance reports, data,
and other documents to figure out what services seem to be in demand.
She also asks her colleagues who work at neighboring states or nearby AJCs
to see what services they think are potentially promising.
Importantly, Teri wants the new grant program
to be informed by the best research evidence out there.
So, Teri uses CLEAR to find and read up on studies about reemployment programs,
and to see which programs and practices have shown evidence of being effective,
especially in settings like hers.
Teri learns from a CLEAR synthesis report on reemployment services
that job search assistance services had favorable impacts on individuals’ employment.
She also finds out that lighter-touch programs, like changing employer contact requirements,
could potentially speed reemployment.
Teri uses CLEAR’s searchable database
to find some of the specific studies listed in the synthesis,
as well as studies on the kinds of services already being used
or recommended by her colleagues.
She reads their short profiles to learn more about where and how
the programs tested were implemented, and what they found.
She uses these studies to identify some new program services
she wants to include in the new reemployment program.
Teri now has an understanding from CLEAR about the practices that research shows
have helped connect people to employment.
She has looked at a variety of evidence and can make an informed decision
about what effective or promising services to include in the new program.
She is confident the new funding will help improve the labor market outcomes
of the people her local AJCs serve.
That’s just one example of how CLEAR makes research on labor interventions
actionable to inform decision-making.
Here’s another example on how CLEAR can help.
Meet Paulo.
Paulo works for his state’s Division of Labor Statistics and Research.
Workplace illnesses and injuries have recently increased in Paulo’s state
and his division has been tasked with hiring an independent evaluator to
(1) assess the effectiveness of inspections on workplace safety and
(2) inform potential improvements to the state’s health and safety inspection strategies.
Paulo understands the learning priorities of this effort
and he wants to be sure his state funds a high-quality study.
Paulo suggests using CLEAR’s Causal Evidence Guidelines
as a way to inform the new study’s design
so it can provide them with the kind of evidence the state seeks.
The guidelines provide the evidence criteria CLEAR uses for rating causal studies;
in other words, the criteria the studies must meet to provide reliable evidence.
He walks through the different designs and concludes the state should ask evaluators
to propose study designs that can produce higher evidence ratings
according to CLEAR standards, such as randomized controlled trials,
interrupted time series, and comparison group designs.
By using CLEAR’s causal evidence guidelines,
Paulo and his division will be able to launch an evaluation
that can provide them with more credible, higher quality evidence
and inform their thinking about how to improve safety in their state.
As these two examples illustrate, CLEAR is a central,
accessible resource you can use to support your decision-making.
For more information on CLEAR, you can visit clear.dol.gov.
To learn more, check out other videos on our website, review CLEAR’s reference documents,
or see our FAQ for quick answers to common questions.
You can also contact us through our website at clear.dol.gov
if you have a specific question about CLEAR.
This concludes our two-part video series on “Using CLEAR to inform decision making.”
You can find links to these videos and other helpful resources about CLEAR
on the Resources page of the CLEAR website.
We hope to stay connected through CLEAR in the future!