Participatory epidemiology: the contribution of participatory research to epidemiology
Bibliographic record
Abstract
BACKGROUND: Epidemiology has contributed in many ways to identifying various risk factors for disease and to promoting population health. However, there is a continuing debate about the ability of epidemiology not only to describe, but also to provide results which can be better translated into public health practice. It has been proposed that participatory research approaches be applied to epidemiology as a way to bridge this gap between description and action. A systematic account of what constitutes participatory epidemiology practice has, however, been lacking. METHODS: A scoping review was carried out focused on the question of what constitutes participatory approaches to epidemiology for the purpose of demonstrating their potential for advancing epidemiologic research. Relevant databases were searched, including both the published and non-published (grey) literature. The 102 identified sources were analyzed in terms of comparing common epidemiologic approaches to participatory counterparts regarding central aspects of the research process. Exemplary studies applying participatory approaches were examined more closely. RESULTS: A highly diverse, interdisciplinary body of literature was synthesized, resulting in a framework comprised of seven aspects of the research process: research goal, research question, population, context, data synthesis, research management, and dissemination of findings. The framework specifies how participatory approaches not only differ from, but also how they can enhance common approaches in epidemiology. Finally, recommendations for the further development of participatory approaches are given. These include: enhancing data collection, data analysis, and data validation; advancing capacity building for research at the local level; and developing data synthesis. CONCLUSION: The proposed framework provides a basis for systematically developing the emergent science of participatory epidemiology.
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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 | 0.364 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.013 | 0.089 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".