Impact of Various Effects of Smoking in the Mouth on Motivating Dental Patients to Quit Smoking
Bibliographic record
Abstract
We explored the impact of addressing personally relevant effects of smoking in the mouth on promoting the motivation to quit in a dental setting at personal and public levels. Stages of behavior change and attempts to quit smoking by smokers were recorded during dental visits. Dentists selected and gave motivational information from 24 topics relevant to a patient’s oral health status, risk, or dental treatment. During the dental visit, each topic was presented to patients. Topics of gingival melanin pigmentation and periodontal disease risk were most frequently presented. Progression through stages of behavior change and attempts to quit smoking were observed after presentation of each topic. At a personal level, progression through stages was most frequently observed after the patient was shown an image of pediatric dental caries and smoker’s palate, and attempts to quit was most frequently observed after the patient shown an image of the effects of smoking cessation and pediatric dental caries. At the public level, enhancing the motivation to progress through stages and attempts to quit was most frequently observed after the presentation of effects of smoking cessation and discoloration of teeth, although the intensity of enhanced motivation significantly correlated with the frequency of presentation, which was not the highest for these topics. Although various smoking effects on the mouth have potential impact on promoting the motivation to quit, the impact on enhancing motivation is not necessarily consistent at personal and public levels.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".