Hormone therapy prescription among physicians in France and Quebec
Bibliographic record
Abstract
OBJECTIVE: Our objective was to compare physician characteristics associated with high-frequency hormone therapy (HT) prescription between gynecologists and general practitioners (GPs) within and between France and Quebec, Canada. DESIGN: A self-administered mail survey was sent to a representative sample of 2,000 physicians in France and 1,000 physicians in Quebec. High-frequency prescribers were those who reported prescribing HT to more than 70% of their postmenopausal patients. The following characteristics were included in the analysis: country, specialty, age, gender, characteristics of the practice (solo or group, private or public, rural or urban, number of patients seen daily, duration of practice, percentage of women 45 years or older), teaching or research activities, participation in education course on HT, and practice patterns relating to menopausal women (having patient education materials available, providing materials to patients, and discussing the possibility of HT). RESULTS: The analysis covered 974 physicians in France (389 GPs and 585 gynecologists) and 452 physicians in Quebec, Canada (318 GPs and 134 gynecologists). Despite differences in health care, in both countries gynecologists were more likely to be high-frequency prescribers than were GPs, although this difference was smaller in Quebec. Canadian physicians were more likely to prescribe HT. The difference between countries was greatest among GPs. Except for nationality and practice patterns designed to provide women with information, none of the physician characteristics was associated with high-frequency prescription among GPs. Among gynecologists, only the number of patients per day and the provision of information were associated with high-frequency prescription. CONCLUSIONS: Notwithstanding a common language, differences in the prescription pattern of HT between countries were greatest at the level of primary care than secondary care. In both countries, specialists were more likely to prescribe HT than were GPs. Implementation of clinical practice guidelines to set baseline standards in the field of menopausal health remains a challenge but will need to take into account cultural characteristics as well as level of medical care.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".