Attitudes and Knowledge Among Obstetrician-Gynecologists Regarding Lesbian Patients and Their Health
Bibliographic record
Abstract
OBJECTIVE: The lesbian patient population is underserved. Almost no research has examined the knowledge and attitudes of obstetrician-gynecologists toward lesbian health. Our study sought to address this research gap. METHODS: All 910 obstetrician-gynecologists licensed in Ontario, Canada, were mailed a true-false survey about lesbian health issues, the Homosexuality Attitudes Scale (HAS), and a demographic survey. RESULTS: Of the 910 surveys, 271 were returned. The mean HAS score was 87.6 (standard deviation [SD] 11.5), indicating an overall positive attitude. The mean knowledge score was 76.0% (SD 9.5), indicating that respondents had adequate knowledge about lesbian health; 22% described their lesbian health knowledge-base as unaware. Most respondents reported lack of education on lesbian health in residency (81%) or medical school (78%). The majority reported a desire for formal education pertaining to lesbian health. There was no correlation between HAS and knowledge scores. CONCLUSIONS: Although our results indicate overall adequate knowledge about lesbian health issues, important knowledge gaps were identified. Medical school and residency training curricula should include formal education about lesbian health issues, particularly because most obstetrician-gynecologists report a desire to receive this information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".