Students working against tobacco: A novel educational program to improve Canadian medical students’ tobacco counselling skills
Bibliographic record
Abstract
BACKGROUND: Medical professionals should be appropriately trained in the field of smoking cessation counseling and be familiar with related tobacco-control issues. Sadly, Canadian medical students receive little education regarding smoking cessation. METHODS: University of Ottawa medical students created Students Working Against Tobacco (SWAT), a program that provides its members with tobacco education and opportunities to discuss tobacco use, smoking prevention and cessation with elementary-school students. Surveys assessing student knowledge and confidence in addressing tobacco issues were administered to the participating students at the start of the program and following their delivery of a school presentation. RESULTS: Students initially lacked knowledge, skills and experience in addressing tobacco issues and discussing smoking prevention and cessation counselling. Following their involvement in the SWAT program, students' smoking cessation counselling knowledge and skills improved, and they expressed confidence in becoming more engaged in this important preventive health issue. CONCLUSION: Until smoking cessation is incorporated into undergraduate medical education programs, gaps will remain in the preparation of tomorrow's physicians regarding the provision of effective smoking cessation counselling and their broader understanding of this important health issue. Currently, there are constraints limiting the number of medical undergraduates that SWAT is able to involve and influence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".