Assessing Bear/Cub/Otter identity and history of cardiovascular disease among gay, bisexual, and other men who have sex with men in Metro Vancouver
Bibliographic record
Abstract
Bear identity exists as an understudied subgroup among gay, bisexual and other men who have sex with men (gbMSM). Given associations between Body Mass Index (BMI) and Bear identity, we examined prevalence estimates and effect modification between Bear identity, BMI, and cardiovascular health. Data is from February 2012–February 2018 and comes from a longitudinal-cohort of gbMSM in Metro Vancouver, recruited using respondent-driven sampling (RDS). We conducted univariable and multivariable logistic regression using RDS weighting. A total of 161 (21.3%) gbMSM self-identified as a Bear/Cub/Otter (BCO) and 48 (7.2%) gbMSM who identified as a BCO had a measured BMI ≥ 30. Multivariable results found non-BCO identity and a BMI ≥ 30 (aOR = 11.27; 95% CI = 2.88, 44.07) was associated with greater odds of history of cardiovascular health condition and/or associated risk factors compared to gbMSM who did not identify as a BCO and had a BMI < 30. The majority of gbMSM with BMI ≥ 30 identified as a BCO. However, BCO identity was not the most significant effect modifier for BMI on a history of a cardiovascular health condition and/or associated risk factors. Interventions should target all gbMSM with increased risk for cardiovascular disease and clinicians should be mindful of culturally sensitive prevention and care for gbMSM who identify as a BCO.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".