Brief smoking cessation interventions by family physicians in northwestern Ontario rural hospitals.
Bibliographic record
Abstract
INTRODUCTION: We report on physicians' beliefs, confidence and clinical practice relative to the provision of smoking cessation interventions in northwestern (NW) Ontario, where tobacco use and tobacco-related disease prevalence are high and smoking cessation services are scarce. METHODS: Physicians working at the 12 rural hospitals in NW Ontario were eligible for inclusion in the study. Survey items included clinical practices based on the "5 A's" protocol for tobacco intervention, and beliefs about, confidence in, and barriers and facilitators to intervention. RESULTS: Physicians from 8 of the 12 hospitals responded. Almost all (> 91%) reported positive beliefs about providing smoking cessation interventions and were confident intervening. Relative to the 5 A's protocol for tobacco intervention, 100% of respondents ask, advise, assess and assist patients to quit smoking, and 89% arrange follow-up. The most frequent methods of assistance included pharmacotherapy, suggestions of specific actions to make it easier to quit and recommendations for alternatives to tobacco use. The most frequent barrier to intervenion was lack of time. DISCUSSION: Based on respondents' positive beliefs, confidence and current clinical practice relative to tobacco interventions, physicians in NW Ontario seem well positioned to play a key role in helping to reduce the high rates of tobacco use and tobacco-related diseases by providing smoking cessation interventions to patients who have been admitted to hospital.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".