Regular health care use by lesbians: a path analysis of predictive factors
Bibliographic record
Abstract
BACKGROUND: Lesbians have more health risks than other women but access preventive medical care less frequently. OBJECTIVE: To test the influence of (i) provider inquiry about sexual orientation, (ii) perceived provider gay-positivity and (iii) patient disclosure of sexual orientation on regular health care use in a sample of Canadian lesbians. METHODS: A path analysis using community survey data from 489 lesbian respondents. RESULTS: 78.5% [95% confidence interval (CI): 74.7-82.0] of women reported regular health service use; 75.8% (95% CI: 72.2-79.8) of women had disclosed their sexual orientation to their provider; and 24.4% (95% CI: 20.6-28.2) of women had been asked about their sexual orientation by their provider. Of those women whose physicians had inquired about their sexual orientation, 100% (95% CI: 97.5-100.0) had disclosed. In the final path analysis, perceived provider gay-positivity and level of patient outness predicted disclosure, which, along with health status predicted regular health care use. All paths were significant at P < 0.05. CONCLUSIONS: Provider-related factors including perceived gay-positivity and inquiry about sexual orientation are strongly associated with disclosure of sexual orientation. Disclosure is associated with regular health care use. Minor changes to practice could improve access to health services for lesbians.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".