Survey of Health Care Providers’ Practices and Opinions Regarding Bacterial Sexually Transmitted Infection Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men
Bibliographic record
Abstract
BACKGROUND: Rates of bacterial sexually transmitted infections (STIs) continue to rise among gay, bisexual, and other men who have sex with men (GBMSM) globally. Testing and treatment can prevent morbidity and transmission. However, testing rates remain suboptimal. METHODS: In 2018, we conducted an online cross-sectional survey to explore STI testing ordering practices, 14 potential barriers for testing and 11 possible ways to improve testing from the perspective of health care providers in Toronto, Ontario. An estimated 172 providers were invited from primary care and sexual health clinic settings. Providers were eligible to complete the survey if they provided care for ≥1 GBMSM per week and were involved in the decision-making process in providing STI tests. We used descriptive statistics to summarize survey responses. RESULTS: Ninety-five providers (55% response rate) participated, of whom 68% worked in primary care and 32% in sexual health settings. Most (66%) saw ≤10 GBMSM clients per week. In primary care (65%) and sexual health (40%) clinic settings, insufficient consultation time was the most common barrier to STI testing. In primary care, other common barriers included difficulty introducing testing during unrelated consultations (53%), forgetting (47%), and patients being sexually inactive (31%) or declining testing (27%). The following were most likely to improve testing: express/fast-track testing services (89%), provider alerts when patients are due for testing (87%), patient-collected specimens (84%), nurse-led STI testing (79%), and standing orders (79%). CONCLUSIONS: Promising interventions to improve bacterial STI testing included initiatives that simplify and expedite testing and expand testing delivery to other health care professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".