Disseminating evidence from health technology assessment: The case of tobacco prevention
Bibliographic record
Abstract
OBJECTIVES: The aims of the present study were to investigate the awareness among dentists and dental hygienists of evidence-based reports and guidelines on tobacco cessation activities and the impact these publications had on clinical practice. METHODS: A questionnaire was mailed to dental hygienists and dentists in Stockholm County, Sweden, and the results were compared with a previous investigation. RESULTS: Among the respondents, awareness of a popular science version of a systematic review on smoking and its effect on oral health was reported by 90 percent of the hygienists and 66 percent of the dentists. The information was used in clinical work by 34 percent of the dentists and 54 percent of the hygienists. Reported changes in patterns of practice were more frequent recommendations to use nicotine replacement therapy and a more widespread use of setting quit dates. Approximately one quarter of the dental professionals reported that they had increased tobacco cessation consultation because of the results from the reports. CONCLUSIONS: Changes in patterns of practice were observed after dissemination of evidence-based information on tobacco cessation. Methods that were proven to be effective in the evidence-based report such as discussing quit dates and recommending nicotine replacement therapy were more commonly used after the publication of the report. Short, popular versions of extensive systematic reviews seem to be useful for implementing evidence-based knowledge and changing clinical practice.
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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 | 0.489 | 0.703 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.027 | 0.047 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.032 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".