Environmental Factors Influencing Adoption of Canadian Guidelines on Smoking Cessation in Dental Healthcare Settings in Quebec: A Qualitative Study of Dentists’ Perspectives
Bibliographic record
Abstract
Background: This study aimed to understand dentists’ perspective of the environmental determinants which positively or negatively influence the implementation of Canadian smoking cessation clinical practice guidelines (5As: Ask-Advise-Assess-Assist-Arrange) in private dental clinics in Quebec. Methods: This study used a qualitative design and an integrative conceptual framework composed of three theoretical perspectives. Data collection was conducted in individual semi-directed interviews with 20 private dentists lasting between 35 and 45 min. The audio-recorded data were transcribed verbatim, followed by a directed content analysis. Results: Some of the barriers identified to counselling in smoking cessation were lack of time, patient attitude, lack of prescription of nicotine replacement therapies, lack of reimbursement, and the lack of training of the dental team. Enablers cited by participants were the style of dentist’s leadership, the availability of community, human and material resources, the perception of counselling as a professional duty, and the culture of dental medicine. In addition to these variables, dentists’ attitude and behaviour were affected by different organisations giving initial or continual training to dentists, governmental policies, and the compatibility of Canadian smoking cessation guidelines with the practice of dentistry. Conclusion: Our findings will inform the development of smoking cessation interventions in dental healthcare settings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".