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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".