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Context matters: measuring implementation climate among individuals and groups.

Sara R Jacobs1, Bryan J Weiner, Alicia C Bunger

  • 1Department of Health Policy and Management, Gillings School of Global Public Health, 1101 McGavran-Greenberg Hall, CB #7411, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7411, USA. jacobssr@live.unc.edu.

Implementation Science : IS
|April 19, 2014
PubMed
Summary
This summary is machine-generated.

Measuring implementation climate is key for effective innovation adoption. This study found it’s a global construct, but measurement level (individual vs. group) depends on the specific organizational context.

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Area of Science:

  • Implementation Science
  • Organizational Psychology
  • Healthcare Innovation

Background:

  • Implementation climate, perceptions of innovation support, is linked to effectiveness.
  • Measurement issues persist regarding its global nature, item referencing (individual/group), and assessment level (group/organizational).

Purpose of the Study:

  • To investigate if implementation climate is a global construct.
  • To determine optimal measurement approaches (individual vs. group-referenced items) and assessment levels (group vs. organizational).

Main Methods:

  • Two cross-sectional survey studies involving physicians and behavioral health clinicians.
  • Confirmatory factor analysis (CFA) to assess global construct nature.
  • Exploratory factor analysis (EFA), correlations, intraclass correlations (ICCs), and interrater agreement (IRA) to evaluate measurement and level.

Main Results:

  • CFA confirmed implementation climate as a global construct (expectations, support, rewards).
  • Measurement level varied: individual-level in dispersed physicians, potentially group-level in co-located clinicians.
  • Implementation climate can be considered an organizational-level construct.

Conclusions:

  • Implementation climate measurement is context-specific.
  • Researchers must consider the study setting when selecting measurement items and levels.
  • Further validation is needed to understand its predictive power for implementation effectiveness.