Physicians’ enquiries into their patients’ alcohol use: public views and recalled experiences
Bibliographic record
Abstract
AIMS: To examine public opinion and experiences of family physician involvement in alcohol use issues and to identify patient characteristics associated with these opinions and experiences. DESIGN: A secondary analysis of population survey data from the 1993 Ontario Alcohol and Other Drug Opinion Survey (OADOS), a random household telephone survey of adults in Ontario, Canada. Opinion and experiences regarding alcohol use were examined by drinking status. PARTICIPANTS: All survey respondents (n = 941; 65% response rate). Population expansion weights were applied to ensure the sample's representativeness of the adult population of Ontario. MEASUREMENTS: Measures assessed the prevalence of opinions and experiences of family physicians: (1) asking patients about their drinking; (2) advising regular drinkers to cut down/quit; and (3) helping patients with alcohol problems. Self-reported past-year alcohol consumption and related problems were used to construct a categorical variable describing current drinking status. FINDINGS: Public opinion supported routine inquiries into patients' drinking habits and advising regular drinkers to cut down. However, the experience of being asked by a physician about drinking, being advised to cut down or being helped with alcohol problems was uncommon. Respondents' drinking status was associated with experiences of being asked about drinking and being advised to cut down. CONCLUSIONS: Physician training should inform physicians that public opinion supports inquiries about drinking and advisement to reduce consumption, as it does not appear that family doctors are meeting these expectations of patients.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".