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Updated: Jan 30, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
Published on: December 9, 2022
Comparison of rapid vs in-depth qualitative analytic methods from a process evaluation of academic detailing in the
Randall C Gale1, Justina Wu1, Taryn Erhardt1
1Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Stanford University, 795 Willow Rd, Menlo Park, CA, 94025, USA.
Background:
It is challenging to conduct and quickly disseminate findings from in-depth qualitative analyses, which can impede timely implementation of interventions because of its time-consuming methods. To better understand tradeoffs between the need for actionable results and scientific rigor, we present our method for conducting a framework-guided rapid analysis (RA) and a comparison of these findings to an in-depth analysis of interview transcripts.
Methods:
Set within the context of an evaluation of a successful academic detailing (AD) program for opioid prescribing in the Veterans Health Administration, we developed interview guides informed by the Consolidated Framework for Implementation Research (CFIR) and interviewed 10 academic detailers (clinical pharmacists) and 20 primary care providers to elicit detail about successful features of the program. For the RA, verbatim transcripts were summarized using a structured template (based on CFIR); summaries were subsequently consolidated into matrices by participant type to identify aspects of the program that worked well and ways to facilitate implementation elsewhere. For comparison purposes, we later conducted an in-depth analysis of the transcripts. We described our RA approach and qualitatively compared the RA and deductive in-depth analysis with respect to consistency of themes and resource intensity.
Results:
Integrating the CFIR throughout the RA and in-depth analysis was helpful for providing structure and consistency across both analyses. Findings from the two analyses were consistent. The most frequently coded constructs from the in-depth analysis aligned well with themes from the RA, and the latter methods were sufficient and appropriate for addressing the primary evaluation goals. Our approach to RA was less resource-intensive than the in-depth analysis, allowing for timely dissemination of findings to our operations partner that could be integrated into ongoing implementation.
Conclusions:
In-depth analyses can be resource-intensive. If consistent with project needs (e.g., to quickly produce information to inform ongoing implementation or to comply with a policy mandate), it is reasonable to consider using RA, especially when faced with resource constraints. Our RA provided valid findings in a short timeframe, enabling identification of actionable suggestions for our operations partner.
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