Bibliographic record
Abstract
In the past, researchers have inadvertently caused stigmatization of various populations, first by not involving community members and then through publishing negative findings. In contrast, participatory research, which is based on a partnership between researchers and those affected by the issue being studied, promotes the voice of those being researched. This essay highlights key principles, processes, complexities, and challenges of participatory research and outlines when participatory research is not appropriate. It also reflects on the training and skills of family physicians that make them especially suited to participatory research. Family physicians have established clinical partnerships with their patients and sometimes entire communities, are trained in patient-centered care-a good basis for community centered research-and are accustomed to working with uncertainty. In addition, they are frequently pragmatic, interested in questions arising from their patients and communities, and likely to respond well to community requests. The main challenges to participatory research are lack of funding, expertise, and time, which may improve as more funding agencies and universities support this approach to research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.182 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".