Substance Use and Dependence Education in Predoctoral Dental Curricula: Results of a Survey of U.S. and Canadian Dental Schools
Bibliographic record
Abstract
The purpose of this study was to obtain information about education in substance use and dependence that appears in the predoctoral curricula of U.S. and Canadian dental schools. Sixty-eight deans were sent a twenty-item survey requesting information about when in the curriculum these subjects were taught, what instructional methods were used, and whether behavior change instruction was included to address these issues in clinical interactions. The survey had an 81 percent response rate. The topics of alcohol use and dependence, tobacco use and dependence, and prescription drug misuse and abuse were reported in over 90 percent (N=55) of responding schools' predoctoral curricula. The topic of other substance use and dependence was reported in only 72.7 percent (N=40) of these schools. The primary instructional method reported was the use of lecture. Less frequently used methods included small-group instruction, instruction in school-based clinic, community-based extramural settings, and independent study. As future health professionals, dental students are an important source for patients concerning substance use, abuse, and treatment. Our investigation confirmed that alcohol, tobacco, and prescription drug abuse is addressed widely in predoctoral dental curricula, but other substance use and dependence are less frequently addressed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".