Establishing the Canadian HIV Women’s Sexual and Reproductive Health Cohort Study (CHIWOS): Operationalizing Community-based Research in a Large National Quantitative Study
Bibliographic record
Abstract
BACKGROUND: Community-based research has gained increasing recognition in health research over the last two decades. Such participatory research approaches are lauded for their ability to anchor research in lived experiences, ensuring cultural appropriateness, accessing local knowledge, reaching marginalized communities, building capacity, and facilitating research-to-action. While having these positive attributes, the community-based health research literature is predominantly composed of small projects, using qualitative methods, and set within geographically limited communities. Its use in larger health studies, including clinical trials and cohorts, is limited. We present the Canadian HIV Women's Sexual and Reproductive Health Cohort Study (CHIWOS), a large-scale, multi-site, national, longitudinal quantitative study that has operationalized community-based research in all steps of the research process. Successes, challenges and further considerations are offered. DISCUSSION: Through the integration of community-based research principles, we have been successful in: facilitating a two-year long formative phase for this study; developing a novel survey instrument with national involvement; training 39 Peer Research Associates (PRAs); offering ongoing comprehensive support to PRAs; and engaging in an ongoing iterative community-based research process. Our community-based research approach within CHIWOS demanded that we be cognizant of challenges managing a large national team, inherent power imbalances and challenges with communication, compensation and volunteering considerations, and extensive delays in institutional processes. It is important to consider the iterative nature of community-based research and to work through tensions that emerge given the diverse perspectives of numerous team members. CONCLUSIONS: Community-based research, as an approach to large-scale quantitative health research projects, is an increasingly viable methodological option. Community-based research has several advantages that go hand-in-hand with its obstacles. We offer guidance on implementing this approach, such that the process can be better planned and result in success.
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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 | 0.172 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".