A community-based case study of the co-construction of an online intervention with gay and bisexual men who use substances
Bibliographic record
Abstract
Collaboration between academic researchers and community members is a cornerstone of community-based research. The success of a project’s results depends on this collaboration. Currently collaborative practices are mostly documented from the researchers’ perspective. Based on a case study of the development of the MyBuzz.ca online intervention for gay and bisexual men who use substances, this article aims to describe components associated with the co-construction process to identify practices that have enabled stronger collaborations with community stakeholders and led to their increased involvement in research. A thematic analysis of eight semi-structured interviews was conducted to identify participants’ perceptions of their participation, their roles, and decision-making with respect to the development of the brief online intervention. Results highlight the importance of establishing prerequisites to foster a positive co-construction experience. Working on an issue that affects the community was one of the elements that encouraged participation in this project. The perspectives of stakeholders (other than academic researchers) support the importance of prerequisites and working on issues affecting the community in successfully conducting community-based research. This study also provides an opportunity to model these elements to foster the co-construction process in community-based research. It highlights facilitators and obstacles to co-construction while underscoring the benefits for various members of the community to participate in this type of study.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".