Reach of GetCheckedOnline among gay, bisexual, transgender and queer men and Two-Spirit people and correlates of use 5 years after program launch in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Understanding who uses internet-based sexually transmitted and blood-borne infection (STBBI) services can inform programme implementation, particularly among those most impacted by STBBIs, including gender and sexual minority (GSM) men. GetCheckedOnline, an internet-based STBBI testing service in British Columbia, Canada, launched in 2014. Our objectives were to assess reach, identify factors associated with use of GetCheckedOnline 5 years into implementation and describe reasons for using and not using GetCheckedOnline among GSM men. METHODS: The Sex Now 2019 Survey was an online, cross-sectional survey of GSM men in Canada administered from November 2019 to February 2020. Participants were asked a subset of questions related to use of GetCheckedOnline. Multivariable binary logistic regression modelling was used to estimate associations between correlates and use of GetCheckedOnline. RESULTS: Of 431 British Columbia (BC) participants aware of GetCheckedOnline, 27.6% had tested using the service. Lower odds of having used GetCheckedOnline were found among participants with non-white race/ethnicity (adjusted OR (aOR)=0.41 (95% CI 0.21 to 0.74)) and those living with HIV (aOR=0.23 (95% CI 0.05 to 0.76)). Those who usually tested at a walk-in clinic, relative to a sexual health clinic, had greater odds of using GetCheckedOnline (aOR=3.91 (95% CI 1.36 to 11.61)). The most commonly reported reason for using and not using GetCheckedOnline was convenience (78%) and only accessing the website to see how the service worked (48%), respectively. CONCLUSION: Over a quarter of GSM men in BC aware of GetCheckedOnline had used it. Findings demonstrate the importance of social/structural factors related to use of GetCheckedOnline. Service promotion strategies could highlight its convenience and privacy benefits to enhance uptake.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".