Enhancement of medical student performance through narrative reflective practice: a pilot project
Bibliographic record
Abstract
BACKGROUND: Narrative Reflective Practice (NRP) is a process that helps medical students become better listeners and physicians. We hypothesized that NRP would enhance students' performance on multiple-choice question exams (MCQs), on objective structured clinical examinations (OSCEs), and on subjective clinical evaluations (SCEs). METHODS: The MCQs, OSCEs and SCEs test scores from 139 third year University of Alberta medical students from the same class doing their Internal Medicine rotation were collected over a 12 month period. All preceptors followed the same one-hour clinical teaching format, except for the single preceptor who incorporated 2 weeks of NRP in the usual clinical teaching of 16 students. The testing was done at the end of each 8-week rotation, and all students within each cohort received the same MCQs, OSCE and SCEs. RESULTS: Independent t-tests were used to assess group differences in the mean MCQ, OSCE and SCE scores. The group receiving NRP training scored 4.7% higher on the MCQ component than those who did not. The mean differences for OSCE and SCE scores were non-significant. CONCLUSIONS: Two weeks NRP exposure produced an absolute increase in students' MCQ score. Longer periods of NRP exposure may also increase the OSCE and SCE scores. This promising pilot project needs to be confirmed using several trained preceptors and trainees at different levels of their clinical experience.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".