HPV vaccination among gay, bisexual and other men who have sex with men in Canada's three largest cities: a person-centred approach
Bibliographic record
Abstract
Starting in 2015, many Canadian provinces and territories introduced publicly-funded human papillomavirus (HPV) vaccination programs targeted to gay, bisexual and other men who have sex with men (GBM) 9–26 years old. Using baseline data from the Engage study, a sexual health study of GBM from three Canadian cities, we explored how social and programmatic factors intersect and affect stages of HPV vaccination (Stage 1: unaware of HPV vaccine, Stage 2: undecided/unwilling to get vaccinated, Stage 3: willing to get vaccinated, Stage 4: vaccinated with at least one dose). First, by city, we created subgroups of GBM ≤26 years old (N Vancouver = 178; Toronto = 123; Montreal = 249) using latent class analysis. Next, by latent class, we estimated the probability of being in the four HPV vaccination stages using the Bolck, Croon and Hagenaar method. Latent class membership was associated with HPV vaccination stage in Vancouver (p = 0.003) and Montreal (p = 0.048) but not Toronto (p = 0.642). In Vancouver and Montreal, membership in the “no barriers” latent class had the highest probability of vaccination (56–58%). In Vancouver, the “racialized, GBM privacy, immigration and healthcare access barriers” class had a 75% probability of being vaccine unaware. In Montreal, the “immigration and past vaccines barriers” and “socio-economic, GBM privacy and healthcare access barriers” classes had the highest probabilities of being vaccine unaware (43% and 46%) and of being undecided or unwilling to get vaccinated (40% and 25%). In conclusion, our person-centred findings suggest tailored interventions by locale may help to increase HPV vaccine uptake among GBM in Canada’s three largest cities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".