Improving medical students’ written communication skills: design and evaluation of an educational curriculum
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Written and verbal communication skills are important skills for all physicians. While verbal skills are taught and assessed in medical school, medical students report limited instruction in written communication skills. This study examined the impact of a curriculum delivered during a 6-week clinical rotation in Internal Medicine on the objective assessment of medical students' written communication skills. METHODS: The curriculum consisted of two educational programmes: a medical student communication tutorial and a resident feedback workshop. The study was conducted from March 2012 to January 2013 at McMaster University in Hamilton, Ontario, Canada. The study featured three arms: (1) control, (2) medical student communication tutorial alone and (3) student tutorial and resident feedback workshop. Data were collected on 126 students during 6-week Internal Medicine clerkship rotations. Students' written consultation notes were collected prior to the educational programmes and at 6 weeks. Blinded faculty assessors used an independently validated Assessment Checklist to evaluate consultation notes. RESULTS: Consultation note scores improved from week 1 to week 6 across all study arms. However, the change was statistically significant only in arm 3, featuring both the medical student tutorial and the resident feedback workshop, with mean scores improving from 4.75 (SD=1.496) to 5.56 (SD=0.984) out of 7. The mean difference between week 1 and week 6 was significantly different (0.806, p=0.002, 95% CI 0.306 to 1.058). CONCLUSIONS: The combination of a resident feedback workshop with medical student written communication tutorial improves objective evaluations of consultation note scores over student tutorial alone.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".