Health Care Utilization in a Sample of Canadian Lesbian Women: Predictors of Risk and Resilience
Bibliographic record
Abstract
This study was designed to test an exploratory path model predicting health care utilization by lesbian women. Using structural equation modeling we examined the joint influence of internalized homophobia, feminism, comfort with health care providers (HCPs), education, and disclosure of sexual identity both in one's life and to one's HCP on health care utilization. Surveys were completed by 254 Canadian lesbian women (54% participation rate) recruited through snowball sampling and specialized media. The majority (95%) of women were White, 3% (n = 7) were women of colour, and the remaining six women did not indicate ethnicity. Participants ranged in age from 18 to 67 with a mean age of 38.85 years (SD = 9.12). In the final path model, higher education predicted greater feminism, more disclosure to HCPs, and better utilization of health services. Feminism predicted both decreased levels of internalized homophobia and increased disclosure across relationships. Being more open about one's sexual identity was related to increased disclosure to HCPs, which in turn, led to better health care utilization. Finally, the more comfortable women were with their HCP the more likely they were to seek preventive care. All paths were significant at p < .01. The path model offers insight into potential target areas for intervention with the goal of improving health care utilization in lesbian women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".