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Gauging treatment impact: The development of exposure variables in a large‐scale evaluation study.

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Title: Gauging treatment impact: The development of exposure variables in a large‐scale evaluation study.
Authors: Maccalla, Nicole M. G.1 (AUTHOR) nicolemaccalla@gmail.com, Purnell, Dawn1 (AUTHOR), McCreath, Heather E.1 (AUTHOR), Dennis, Robert A.1 (AUTHOR), Seeman, Teresa1 (AUTHOR)
Source: New Directions for Evaluation. Jun2022, Vol. 2022 Issue 174, p57-68. 12p.
Subject Terms: *Diversity training programs, Medical research, Gaging
Company/Entity: National Institutes of Health (U.S.)
Abstract: While guidance on how to design rigorous evaluation studies abounds, prescriptive guidance on how to include critical process and context measures through the construction of exposure variables is lacking. Capturing nuanced intervention dosage information within a large‐scale evaluation is particularly complex. The Building Infrastructure Leading to Diversity (BUILD) initiative is part of the Diversity Program Consortium, which is funded by the National Institutes of Health. It is designed to increase participation in biomedical research careers among individuals from underrepresented groups. This chapter articulates methods employed in defining BUILD student and faculty interventions, tracking nuanced participation in multiple programs and activities, and computing the intensity of exposure. Defining standardized exposure variables (beyond simple treatment group membership) is crucial for equity‐focused impact evaluation. Both the process and resulting nuanced dosage variables can inform the design and implementation of large‐scale, diversity training program, outcome‐focused, evaluation studies. [ABSTRACT FROM AUTHOR]
Copyright of New Directions for Evaluation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: *<searchLink fieldCode="DE" term="%22Diversity+training+programs%22">Diversity training programs</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+research%22">Medical research</searchLink><br /><searchLink fieldCode="DE" term="%22Gaging%22">Gaging</searchLink>
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  Data: While guidance on how to design rigorous evaluation studies abounds, prescriptive guidance on how to include critical process and context measures through the construction of exposure variables is lacking. Capturing nuanced intervention dosage information within a large‐scale evaluation is particularly complex. The Building Infrastructure Leading to Diversity (BUILD) initiative is part of the Diversity Program Consortium, which is funded by the National Institutes of Health. It is designed to increase participation in biomedical research careers among individuals from underrepresented groups. This chapter articulates methods employed in defining BUILD student and faculty interventions, tracking nuanced participation in multiple programs and activities, and computing the intensity of exposure. Defining standardized exposure variables (beyond simple treatment group membership) is crucial for equity‐focused impact evaluation. Both the process and resulting nuanced dosage variables can inform the design and implementation of large‐scale, diversity training program, outcome‐focused, evaluation studies. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of New Directions for Evaluation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/ev.20509
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        Text: English
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              Text: Jun2022
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