Doing Participatory Action Research in a Multicase Study
Bibliographic record
Abstract
In this article, we describe an approach for conducting participatory action research (PAR) in a longitudinal multicase study, with particular focus on cross-case analysis. Existing literature has documented the practice of PAR in single-case studies, but far less has been written on how to conduct PAR across multiple cases. There is also a need for instructional examples of multicase study application, particularly methods of cross-case analysis. In PAR, research methods—including data analysis methods—have the power to shape participant inclusion or exclusion, involvement or attrition, and mobilization of knowledge in real time. In response to these challenges, we discuss the analysis methods used in a PAR study of health leadership in Canada. The project, which consisted of six case studies of leadership in major health system change, involved health leaders as collaborators. We address the challenges of doing PAR with collaborators facing time limitations and suggest a project structure for involving collaborators at critical junctures. We present a detailed, two-part method for conducting cross-case data analysis. Our method involved targeted collaborator involvement in data interpretation while also ensuring faithfulness to the coded data. We describe our process for mobilizing study findings through a deliberative dialogue with health leaders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.297 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".