The pursuit of excellence: engaging the community in participatory health research
Bibliographic record
Abstract
Community-based participatory research approaches are designed to improve health and well-being in communities and to minimize health disparities in general. It is this partnership approach to research that equitably involves community members, organizational representatives and researchers in all aspects of the research process and in which all partners contribute expertise, decision-making and ownership. Further to this, community-based participatory research is utilized to study and address community-identified issues through a collaborative and empowering action-oriented process that builds on the strengths of the community. The results of this research endeavour highlight the need for integrating community-based participatory research, primary health care and social accountability in the pursuit of excellence. The process and the results/findings provide ways that the community are able to enhance their health and wellness, increase capacity and be empowered to direct their education, research and service activities towards addressing and meeting the health priorities of the community.
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 | 0.321 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".