Online Intervention as Strategy to Reach Men Who Have Sex With Other Men and Who Use Substances in a Sexual Context. Development of the MONBUZZ.ca Project
Bibliographic record
Abstract
Men who have sex with men (MSM) use more psychoactive substances and a greater variety of them compared to their heterosexual peers. In this population, substance use is particularly characterized by polydrug use, binge, and sexualized substance use. MSM who use substances do not recognize themselves in public health messages targeting substance users. In addition, they recognize their problematic substance use later than heterosexuals and, as a result, they use addiction services later in their addiction trajectories. When accessing addiction services, the links between drug use and sexual life are rarely considered. Because of this profile, online interventions are a promising way to reach this hard-to-reach population. Currently available online interventions targeting MSM address the topics of substance use and sexual life separately. To deal with this situation, our team wanted to develop an online intervention platform for MSM who use substances in a sexual context. Given that online addiction interventions do not address sex and that MSM drug use is highly related to sexual activity, we first explored the literature related to online interventions targeting MSM and HIV risk behaviors, as well as online interventions targeting general population in order to: (1) identify relevant (or personalized) intervention methods; (2) describe the approaches used; and (3) describe their effects. Second, we turned to the literature to develop the MONBUZZ.ca project in collaboration with community organizations. The results of the narrative review provided a critical portrait of online interventions for MSM and guided the development process of MONBUZZ.ca. We discuss issues of co-development of a research and brief intervention tool based on promising practices as well as challenges of its implementation and evaluation.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".