Who Benefits From Community-Based Participatory Research? A Case Study of the Positive Youth Project
Bibliographic record
Abstract
Community-based participatory research (CBPR) has evolved as a popular new paradigm in health research. This shift is exciting, yet there is still much to discover about how various stakeholders are affected. This article uses a critical social science perspective to explore who benefits from these changes through an analysis of a CBPR case study (The Positive Youth Project). Two major categories of beneficiaries emerged: the research itself and the partner-stakeholders. The benefits, however, were not gained without substantial human resource investment, nor were they necessarily equitably spread. Participation costs included heavy demands of time, an added burden of work, frustration with the process, missing other opportunities, risking loss of anonymity, and loss of control. Care needs to be taken to ensure that concrete benefits accrue for all project partners and costs are minimized. Another way of framing benefits is to look at the community capacities built to address future health and social issues.
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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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".