Teaching tobacco dependence treatment and counseling skills during medical school: Rationale and design of the Medical Students helping patients Quit tobacco (MSQuit) group randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Physician-delivered tobacco treatment using the 5As is clinically recommended, yet its use has been limited. Lack of adequate training and confidence to provide tobacco treatment is cited as leading reasons for limited 5A use. Tobacco dependence treatment training while in medical school is recommended, but is minimally provided. The MSQuit trial (Medical Students helping patients Quit tobacco) aims to determine if a multi-modal and theoretically-guided tobacco educational intervention will improve tobacco dependence treatment skills (i.e. 5As) among medical students. METHODS/DESIGN: 10 U.S. medical schools were pair-matched and randomized in a group-randomized controlled trial to evaluate whether a multi-modal educational (MME) intervention compared to traditional education (TE) will improve observed tobacco treatment skills. MME is primarily composed of TE approaches (i.e. didactics) plus a 1st year web-based course and preceptor-facilitated training during a 3rd year clerkship rotation. The primary outcome measure is an objective score on an Objective Structured Clinical Examination (OSCE) tobacco-counseling smoking case among 3rd year medical students from schools who implemented the MME or TE. DISCUSSION: MSQuit is the first randomized to evaluate whether a tobacco treatment educational intervention implemented during medical school will improve medical students' tobacco treatment skills. We hypothesize that the MME intervention will better prepare students in tobacco dependence treatment as measured by the OSCE. If a comprehensive tobacco treatment educational learning approach is effective, while also feasible and acceptable to implement, then medical schools may substantially influence skill development and use of the 5As among future physicians.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".