A call for an International Collaboration on Participatory Research for Health
Bibliographic record
Abstract
Participatory health research (PHR) has emerged as an important approach for addressing local health issues, including building capacity for health promotion. Increasingly, PHR is drawing the attention of communities, funders, decision-makers and researchers worldwide. It is time to consolidate what we know about PHR in order to secure its place as a source of knowledge and action for public health. This can be achieved through an International Collaboration on Participatory Research for Health to addresses the following issues:Set a framework in which information can be exchanged, decisions can be reached and information can be disseminated on central issues in PHR. Provide an international forum to discuss standards and quality. Produce guidelines for researchers, practitioners and community members. Synthesize the findings of PHR internationally. Formulate recommendations regarding generalizable findings. Similar to the Cochrane Collaboration on clinical trials research, the PHR Collaboration will be dependent on a host of experts from various countries to bring together what we know about PHR and to make that knowledge accessible to an international audience. Unlike the Cochrane Collaboration, the PHR Collaboration will include both quantitative and qualitative research approaches. The goal of the PHR Collaboration will not be able to achieve a standardization of research protocols, but rather to find meaningful ways to judge the quality of PHR and to report on its findings while respecting the variety of locally based approaches to research design, data collection and interpretation.
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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 | 0.457 | 0.362 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.030 | 0.041 |
| Open science | 0.008 | 0.075 |
| Research integrity | 0.035 | 0.069 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".