Social-ecological factors associated with having a regular healthcare provider among lesbian, gay, bisexual and transgender persons in Jamaica
Bibliographic record
Abstract
Lesbian, gay, bisexual, and transgender (LGBT) people experience a multitude of barriers to healthcare access, particularly in highly stigmatising contexts, such as Jamaica. Access to a regular healthcare source can contribute to uptake of health knowledge and services. Yet social-ecological factors associated with access to a regular healthcare provider among LGBT persons in Jamaica are underexplored. We conducted a cross-sectional survey with LGBT persons in Jamaica to examine socio-demographic and social-ecological factors associated with having a regular healthcare provider. Nearly half (43.6%) of 911 participants reported having a regular healthcare provider. In multivariate analyses, socio-demographic factors (higher age; identifying as a cisgender sexual minority man or woman compared to a transgender woman) were associated with higher odds of having a regular healthcare provider. Socio-demographic (living in Ocho Rios or Montego Bay compared to Kingston), stigma/discrimination (a bad past healthcare experience), and structural factors (insufficient money for housing; reporting medication costs as a barrier; no health insurance) were associated with decreased odds of having a regular healthcare provider. Findings support a multi-level approach to understanding and addressing barriers to having a regular healthcare provider among LGBT people in Jamaica.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".