A Realist Approach to Analysis in a Participatory Research Project
Bibliographic record
Abstract
BACKGROUND: Realist reviews have shown the effectiveness of participatory action research but the realist approach has not been used in combination with a participatory approach in qualitative data analysis. OBJECTIVES: To study the links between preexisting conditions in neighborhoods and the kind of actions taken at the community level during the coronavirus disease 2019 pandemic in Toronto, a community-university research partnership used a critical realist approach to analyze qualitative interviews with grassroots leaders. This article describes the procedures developed to enable participation of the full community- academic team in the analysis. METHODS: One analyst coded paragraphs in all 46 interviews for preexisting conditions (contexts), actions taken (intervention components), the often implicit factors that underpinned the actions (mechanisms), and observed results (outcomes) as stated by the interviewees. Each interview was summarized in terms of the contexts (C), actions (I), mechanisms (M) and outcomes (O) identified and one to seven midrange CIMO hypotheses were developed for each interview. A second level of analysis involved sense-making workshops with the community partner and a cross-section of interviewees using the CIMO statements. CONCLUSIONS: This article describes the realist approach to analysis and the changes that were made to enable a mixed team of community leaders and academics to generate overall statements of impact. This is a novel approach to qualitative data analysis, with a range of implications for the use of this technique in participatory research.
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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 | 0.242 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".