Human Papillomavirus (HPV) Vaccination Among Gay, Bisexual, and Other Men Who Have Sex with Men and Men Living with HIV
Bibliographic record
Abstract
Gay, bisexual, and other men who have sex with men (GBM) and men living with HIV are disproportionately affected by HPV compared to the general population. The Canadian National Advisory Committee on Immunization recommends HPV vaccination for all GBM and people living with HIV. Starting in 2015, some of the Canadian provinces and territories began introducing publicly-funded HPV vaccination programs for GBM ≤26 years old and people living with HIV. Four research objectives were developed to 1) determine the uptake of vaccination programs and national recommendations, and 2) identify key barriers that need be overcome to increase vaccine coverage. Two data sources were used to address these objectives: a sample of 1677 older men living with HIV from the Ontario HIV Treatment Network Cohort Study (OCS) and a community-recruited sample of 2449 GBM from Vancouver, Toronto, and Montreal from the Engage Study. The first two objectives quantified and determined factors associated with HPV vaccine initiation. The last two objectives explored the association between social and programmatic barriers and facilitators and a multi-stage cascade of vaccine uptake where vaccine awareness and willingness to get vaccinated were considered among unvaccinated men. Only 7% of men living with HIV from the OCS had initiated vaccination. Among GBM from the Engage Study, 26-35% of men ≤26 years old and 7-26% of men ≥27 years old across the cities had initiated vaccination. There were clear social inequities among unvaccinated older men ineligible for publicly-funded vaccine, such as lack of insurance coverage and lower socio-economic status (SES), and lower initiation was seen among Latin American GBM and racialized men living with HIV. Among GBM, accessing sexual healthcare services was associated with vaccine initiation. GBM unaware of the HPV vaccine or undecided/unwilling to get vaccinated faced many barriers to vaccination, such as sexual orientation non-disclosure, not accessing healthcare, and lower SES. The combination of barriers experienced differed across cities and subgroups of men, suggesting the need for tailored interventions. To increase vaccination coverage in these populations, increasing knowledge of the vaccine, and reducing social inequities and barriers introduced by publicly-funded program eligibility requirements should be a priority.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".