How does participatory research work: protocol for a realist synthesis
Bibliographic record
Abstract
INTRODUCTION: Participatory research science deals with partnerships underlying research, governance and ownership of research products. It is concerned with relationships behind research objectives and methods. Participatory research has gained significant traction in design of health interventions, contextualising these to local settings and stakeholder groups. Despite a massive increase in participatory research exercises, the field remains undertheorised, and the mechanisms for improving health outcomes remain unclear. This realist review seeks to understand how and under what circumstances participatory research impacts health and social outcomes. METHODS AND ANALYSIS: The review will follow four steps: (1) searching for and selecting evidence, (2) assessing the quality of evidence, (3) extracting and categorising data and (4) synthesising the data in the form of context-mechanism-outcomes configurations. The review will follow the Realist And Meta Narrative Evidence Syntheses: Evolving Standards (RAMESES) II guidelines for reporting realist evaluations. We categorise and synthesise data in four steps: (1) identifying outcomes, (2) identifying contextual components of outcomes, (3) theoretical redescription (abduction) and (4) identifying mechanisms. A retroductive analysis will identify mechanisms by moving between empirical data and theories, using inductive and deductive reasoning to explain the outcomes-context matches. The output will generate middle-range theories on how participatory research works, for whom and under what circumstances. ETHICS AND DISSEMINATION: This study is a review of a published literature. It does not involve human participants. We will convene a workshop to share and discuss the preliminary results with partners and key stakeholders involved in participatory health research. We will publish the review results in peer-reviewed journals and academic conferences.
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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 | 0.225 | 0.387 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.137 | 0.033 |
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".