What Goes Around: the process of building a community-based harm reduction research project
Bibliographic record
Abstract
BACKGROUND: Often, research takes place on underserved populations rather than with underserved populations. This approach can further isolate and stigmatize groups that are already made marginalized. What Goes Around is a community-based research project that was led by community members themselves (Peers). CASE PRESENTATION: This research aimed to implement a community-based research methodology grounded in the leadership and growing research capacity of community researchers and to investigate a topic which community members identified as important and meaningful. Chosen by community members, this project explored how safer sex and safer drug use information is shared informally among Peers. Seventeen community members actively engaged as both community researchers and research participants throughout all facets of the project: inception, implementation, analysis, and dissemination of results. Effective collaboration between community researchers, a community organization, and academics facilitated a research process in which community members actively guided the project from beginning to end. CONCLUSIONS: The methods used in What Goes Around demonstrated that it is not only possible, but advantageous, to draw from community members' involvement and direction in all stages of a community-based research project. This is particularly important when working with a historically underserved population. Purposeful and regular communication among collaborators, ongoing capacity building, and a commitment to respect the experience and expertise of community members were essential to the project's success. This project demonstrated that community members are highly invested in both informally sharing information about safer sex and safer drug use and taking leadership roles in directing research that prioritizes harm reduction in their communities.
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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 | 0.181 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.042 | 0.021 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.009 | 0.038 |
| Research integrity | 0.009 | 0.029 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".