Best Practices for Smoking Cessation Interventions in Primary Care
Bibliographic record
Abstract
BACKGROUND: In Canada, smoking is the leading preventable cause of premature death. Family physicians and nurse practitioners are uniquely positioned to initiate smoking cessation. Because smoking is a chronic addiction, repeated, opportunity-based interventions are most effective in addressing physical dependence and modifying deeply ingrained patterns of beliefs and behaviour. However, only a small minority of family physicians provide thorough smoking cessation counselling and less than one-half offer adjunct support to patients. OBJECTIVE: To identify the key steps family physicians and nurse practitioners can take to strengthen effective smoking cessation interventions for their patients. METHODS: A multidisciplinary panel of health care practitioners involved with smoking cessation from across Canada was convened to discuss best practices derived from international guidelines, including those from the United States, Europe, and Australia, and other relevant literature. The panellists subsequently refined their findings in the form of the present article. RESULTS: The present paper outlines best practices for brief and effective counselling for, and treatment of, tobacco addiction. By adopting a simple series of questions, taking 30 s to 3 min to complete, health care professionals can initiate smoking cessation interventions. Integrating these strategies into daily practice provides opportunities to significantly improve the quality and duration of patients' lives. CONCLUSION: Tobacco addiction is the most important preventable cause of morbidity and mortality in Canada. Family physicians, nurse practitioners and other front-line health care professionals are well positioned to influence and assist their patients in quitting, thereby reducing the burden on both personal health and the public health care system.
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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 | 0.048 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".