Developing Theory From Complexity: Reflections on a Collaborative Mixed Method Participatory Action Research Study
Bibliographic record
Abstract
Research studies are increasingly complex: They draw on multiple methods to gather data, generate both qualitative and quantitative data, and frequently represent the perspectives of more than one stakeholder. The teams that generate them are increasingly multidisciplinary. A commitment to engaging community members in the research process often adds a further layer of complexity. How to approach a synthesizing analysis of these multiple and varied data sources with a large research team requires considerable reflection and dialogue. In this article, we outline the strategies used by one multidisciplinary team committed to a participatory action research (PAR) approach and engaged in a mixed method program of research to synthesize the findings from four subprojects into a conceptual framework that could guide practice in community mental health organizations. We also summarize factors that hold promise for increasing productivity when managing complex research projects.
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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 | 0.248 | 0.254 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.040 | 0.097 |
| Scholarly communication | 0.045 | 0.040 |
| Open science | 0.012 | 0.041 |
| Research integrity | 0.022 | 0.040 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".