Steps for Meaningful Community Partnership in Research: An Intersectional Feminist Research Program Case Study
Bibliographic record
Abstract
The historical focus of the HIV movement on men who have sex with men has led to the systematic exclusion of women from research, programming, and decision-making. In the early 2000s, women researchers, advocates, and community leaders drove transformative shifts in Canada’s HIV sector through community-based participatory research (CBPR) approaches. Their use of CBPR not only revolutionized women’s engagement but also propelled significant progress towards gender-equitable research, including with trans communities and gender diverse and expansive persons. In this article, we critically examine the history of CBPR, specifically in the HIV field, from an intersectional feminist lens. We then present a case study of our research program: the Women and HIV Research Program, as a framework for meaningful community partnership. Next, as academics and community leaders, we describe the conceptualization of meaningful community-engaged research that we developed over 20 years. Our research program has been built upon a strong foundation of genuine academic-community partnerships and has embraced co-creation as a core principle. We reflect on the changes we have seen and responded to in the field over time. Our goal is for this article to serve as a reflective blueprint for those interested in meaningful community engagement and partnership in research.
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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 | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".