Do the correlates of smoking cessation counseling differ across health professional groups?
Bibliographic record
Abstract
INTRODUCTION: Smoking cessation counseling by health professionals is an effective approach to increase cessation rates among smokers. To guide the development of training and educational interventions, we surveyed six health professional groups including general practitioners (GPs), pharmacists, dentists, dental hygienists, nurses, and respiratory therapists, in order to describe current practices and identify the correlates of smoking cessation counseling. METHODS: Self-administered questionnaires were mailed to 500 persons randomly selected from the membership lists of active licensed professionals in each health professional group in Québec. RESULTS: Response proportions ranged from 52% (nurses) to 70% (dental hygienists). Compared with other groups, GPs and pharmacists undertook more counseling with patients ready to quit. GPs and respiratory therapists undertook more counseling with patients not ready to quit. Three factors emerged consistently across most groups as positively associated with counseling, including the belief that counseling is the role of health professionals, perceived self-efficacy to engage in effective counseling, and knowledge of community cessation resources. DISCUSSION: The correlates of cessation counseling are similar across health professional groups. Interventions that address beliefs that cessation counseling is the role of health professionals, self-efficacy to provide effective counseling, and knowledge of community resources may result in improved cessation counseling practices among health professionals.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".