EFFECTIVENESS OF MEDICAL STUDENT COUNSELING FOR HOSPITALIZED PATIENTS ADDICTED TO TOBACCO (MS-CHAT): A RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
A. Khetan is a co-founder of SEHAT (Society to Enhance Health and Access to Treatments), Dalkhola, West Bengal, India. SEHAT provided funding for this study. Other authors have no relevant financial disclosures. ASCVD/CVD Risk Factors; Preventive Cardiology Best Practices The need for, and effectiveness of physician counseling for tobacco has been well emphasized. However, the medical curriculum in many countries offers very little training needed to offer effective behavioral counseling. We hypothesized that providing medical students with experiential training in tobacco cessation counseling will improve their knowledge, while providing a valuable resource to help patients quit. pandemic, the primary outcome was changed from a biochemically verified quit rate to self-reported 7-day point prevalence of smoking cessation at 6 months. Changes in medical student knowledge were assessed using a pre- and post-questionnaire delivered prior to and 12 months after training. Among 688 patients randomized across three medical schools, 343 were assigned to the intervention group and 345 to the control group. After 6 months of follow up, the primary outcome occurred in 188 patients (54.8%) in the intervention group, and 145 patients (42.0%) in the control group (absolute difference 12.8%; relative risk, 1.67; 95% confidence interval, 1.24-2.26; p <0.001). Among 70 medical students who participated in the study, knowledge increased from a mean score of 14.8 (±0.8, maximum score of 25) at baseline to a score of 18.1 (±0.8) at 12 months, an absolute mean difference of 3.3 (95% CI, 2.3-4.3; p <0.001). This is an effective, low cost intervention that achieves the dual purpose of providing experiential training in behavioral counseling to future physicians, while simultaneously helping tobacco users quit. It is easily scalable and can be tailored to meet the needs of medical education and tobacco cessation programs in health systems across the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".