Applying a theory-based approach to identify determinants of dentists’ intention to adopt Canadian smoking cessation clinical guidelines in Quebec: A pilot study
Bibliographic record
Abstract
Introduction: Tobacco is a risk factor for many oral and systemic diseases and conditions. Many institutions recommend the use of clinical guidelines on smoking cessation to help people quit smoking. Yet, several studies also indicate that clinical guidelines are widely underutilized and study of this topic through a theoretical framework are scarce. The purpose of this study was to identify the underlying individual mechanisms which support dentists’ intention to adopt the Canadian smoking cessation clinical guidelines in Quebec, Canada.Methods: A cross-sectional study was carried out on a sample of 59 dentists working in private practices between January and September 2016. The study was conducted using a validated and anonymous questionnaire based on a modified version of the Triandis’ Theory of Interpersonal Behaviour. The theoretical model was tested by multiple linear regression.Results: The adapted theoretical model explained 63% of variance in dentists’ intention to adopt these guidelines in their practices. The mains predictors of dentists’ intention were professional norm (β=0.85; p≤ 0.0001) and control beliefs (β=0.3; p= 0.01).Conclusions: Grounded in the theory, the results of this study give a first view of dentists’ individual determinants that could be targeted to develop successful ways of increasing the adoption of Canadian smoking cessation guidelines in dental settings. Further research is needed to confirm these results.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".