Postgraduate education for doctors in smoking cessation
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Smoking cessation advice from doctors helps improve quit rates but the opportunity to provide this advice is often missed. Postgraduate education is one strategy to improve the amount and quality of cessation support provided. This paper describes a sample of postgraduate education programs for doctors in smoking cessation and suggests future directions to improve reach and quality. DESIGN AND METHODS: Survey of key informants identified through tobacco control listserves supplemented by a review of the published literature on education programs since 2000. Programs and publications from Europe were not included as these are covered in another paper in this Special Issue. RESULTS: Responses were received from only 21 key informants from eight countries. Two further training programs were identified from the literature review. The following components were present in the majority of programs: 5 As (Ask, Advise, Assess, Assist and Arrange) approach (72%), stage of change (64%), motivational interviewing (72%), pharmacotherapies (84%). Reference to clinical practice guidelines was very common (84%). The most common model of delivery of training was face to face. Lack of interest from doctors and lack of funding were identified as the main barriers to uptake and sustainability of training programs. DISCUSSION AND CONCLUSIONS: Identifying programs proved difficult and only a limited number were identified by the methods used. There was a high level of consistency in program content and a strong link to clinical practice guidelines. Key informants identified limited reach into the medical profession as an important issue. New approaches are needed to expand the availability and uptake of postgraduate education in smoking cessation
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".