Lifestyle health risk assessment. Do recently trained family physicians do it better?
Bibliographic record
Abstract
OBJECTIVE: To determine whether recently trained family physicians were more likely to routinely assess lifestyle health risks during general medical evaluations. To document physicians' perceptions of the difficulties of lifestyle risk assessment, of medical training in that area, and of how often they saw patients with lifestyle health risks. DESIGN: Anonymous mailed survey conducted in 1995. SETTING: Family practices in the province of Quebec. PARTICIPANTS: Stratified random sample of 805 active family physicians of 1111 surveyed; 25 were ineligible or could not be located, and 281 did not respond (74.1% response rate). MAIN OUTCOME MEASURES: Proportion of physicians graduating before and after 1989 who reported routinely (with 90% or more of their patients) assessing their adult and adolescent patients during general medical evaluations for substance use, sexual risk behaviours, and history of family violence and sexual abuse. RESULTS: Except for asking about drug use, recently trained family physicians did not report better assessment of lifestyle health risks during general medical examinations than family physicians who graduated more than 10 years ago did. In both groups, routine assessment averaged 82% for tobacco use, 68% for alcohol consumption, and 20% to 40% for sexual risk behaviours. Screening for family violence and sexual abuse was rare, but more frequently reported by older women physicians. Only 20% to 40% of recent graduates rated their medical training adequate for evaluating illicit drug use, family violence, and sexual abuse. CONCLUSION: Recently trained family physicians do not assess most lifestyle risk factors any better than their more experienced colleagues.
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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 | 0.002 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".