Physician Behavior Towards Male and Female Problem Drinkers
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that physicians are less likely to identify alcohol problems in females than in males. PURPOSE: To compare the performance of family medicine residents with male and female simulated patients (SPs) posing as problem drinkers. METHODS: Fifty-six family medicine residents completed a baseline survey on knowledge and attitudes towards problem drinkers. Each resident was then visited by one male and female unannounced SP. The male and female roles were similar with respect to presenting complaint (in somnia or hypertension), age, social class, and drinking history. RESULTS: Residents expressed slightly more positive attitudes towards female than male patients (3.32 vs. 3.09, p < .001). Residents scored higher with undetected male than with undetected female SPs on the assessment checklist (5.1 vs. 3.2, p < .045), the management checklist (4.4 vs. 3.2, p = .032), and an interpersonal rating scale (the Alcohol Skills Rating Form; 5.5 vs. 4.7, p = .023). CONCLUSION: Educational programs should focus on improving physicians' clinical skills in the identification and treatment of alcohol problems in 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".