Teaching Residents How to Effectively Prescribe Nicotine Replacement Therapy on the Clinical Teaching Unit
Bibliographic record
Abstract
Cigarette smoking is the leading cause of preventable death. On the Clinical Teaching Unit, medical students and residents are well positioned to provide smoking cessation resources to patients with the aim of improving quit rates. However, there is a paucity of data evaluating their role in counselling patients on smoking cessation. A survey at our centre identified that medical trainees had a lack of knowledge regarding Nicotine Replacement Therapy (NRT) as a barrier to smoking cessation counselling. We hypothesized that a teaching session on NRT during an inpatient Internal Medicine rotation would increase learner comfort in prescribing these products. Medical trainees on the Internal Medicine Clinical Teaching Unit (CTU) attended a teaching session during week 4 of an 8-week rotation. Pharmacy records from the 8-week period were retrospectively analyzed to determine NRT prescribing behaviour. Pre-intervention, 5.8% (13/225) of new admissions received an NRT prescription. Post-intervention, 17% (31/182) of new admissions received an NRT prescription. Using a Fisher’s exact test, the percentage of new admissions that received a prescription was significantly different (p<0.001) between the pre- and post-intervention time frames. This data suggests that integrating education on NRT into CTU teaching can significantly alter prescribing behaviour and improve access to NRT for patients who need it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".