Community and provider preferences for STI testing interventions for sexual minority men in Toronto, Canada
Bibliographic record
Abstract
Context: Canadian clinical guidelines recommend at least annual and up to quarterly testing for bacterial sexually transmitted infections (STI) among sexual minority men (SMM). However, testing rates are suboptimal. Objective: To build consensus regarding interventions with the greatest potential for improving local STI testing services for SMM communities using a web-based “e-Delphi” process. Study Design and Analysis: The e-Delphi used successive survey rounds, with feedback in between rounds, to determine priorities among groups using a panel format. We recruited 2 expert panels: community members/SMM who sought/underwent STI testing in the preceding 18 months (09/2019-11/2019); and healthcare providers who offered STI testing to SMM in the past 12 months (02/2020-05/2020). Experts prioritized 6-8 interventions using a 7-point Likert scale from ‘definitely not a priority’ to ‘definitely a priority’ over three survey rounds. Setting or Dataset: Toronto, Canada Population Studied: Gay, bisexual and other men who have sex with men Intervention/Instrument: Web-based “e-Delphi” Outcome Measures: Consensus was defined as ≥60% within a ±1 response point. We report the percentage agreeing that an intervention is ‘somewhat a priority/a priority/definitely a priority’ at the final survey round. Results: For the Community Experts, 43/51 (84%) completed all rounds; 19% were living with HIV, 37% HIV-negative on Pre-Exposure Prophylaxis (PrEP), 42% HIV-negative not on PrEP. We reached consensus on six interventions: Client reminders (95%), Express testing (88%), Routine testing (83%), Online booking app (83%), Online testing (77%) and Nurse-led testing (72%). Community Experts favored interventions that were convenient yet maintained a relationship with their provider. For the Provider Experts, 37/48 (77%) completed all rounds; 59% were primary care physicians. Consensus was reached on the preceding six interventions (range 68%-100%), but not for Provider Alerts (19%) and Provider Audit and Feedback (16%). Express, Online and Nurse-led testing were prioritized by >95% of Provider Experts because of streamlined processes and less need to see a provider. Conclusions: Both panels were enthusiastic about innovations that make STI testing more efficient. However, Community Experts preferred convenient interventions that involved their provider, while Provider Experts favored interventions that prioritized reduced patient-provider time.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".