Impact of a Dental/Dental Hygiene Tobacco‐Use Cessation Curriculum on Practice
Bibliographic record
Abstract
Tobacco use is the chief avoidable cause of morbidity and mortality in North America and is associated with increased risk for oral cancer and increased prevalence and severity of periodontitis and other oral conditions. By delivering two- to three-minute tobacco-use cessation counseling (TUCC), oral health professionals can achieve quit rates substantially higher than the spontaneous quit rate. However, many clinicians report lack of training and knowledge in TUCC as barriers to providing cessation counseling. The purpose of this study was to evaluate whether implementation of a comprehensive, dental school-based, tobacco-use cessation program would increase the extent to which tobacco-using patients received TUCC. The school's program was based on the critical administrative, cultural, structural, and policy components of effective TUCC interventions outlined by Fiore et al. A pre- and post-program telephone interview of tobacco-using patients assessed TUCC intervention by students. A significantly greater proportion of patients received TUCC post-program compared to pre-program in terms of consequences associated with tobacco use as well as advice to quit. A comprehensive TUCC program resulted in an improvement of 11.7 percent for consequences and 23 percent for advice to quit.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".