A prospective randomized trial of content expertise versus process expertise in small group teaching
Bibliographic record
Abstract
BACKGROUND: Effective teaching requires an understanding of both what (content knowledge) and how (process knowledge) to teach. While previous studies involving medical students have compared preceptors with greater or lesser content knowledge, it is unclear whether process expertise can compensate for deficient content expertise. Therefore, the objective of our study was to compare the effect of preceptors with process expertise to those with content expertise on medical students' learning outcomes in a structured small group environment. METHODS: One hundred and fifty-one first year medical students were randomized to 11 groups for the small group component of the Cardiovascular-Respiratory course at the University of Calgary. Each group was then block randomized to one of three streams for the entire course: tutoring exclusively by physicians with content expertise (n = 5), tutoring exclusively by physicians with process expertise (n = 3), and tutoring by content experts for 11 sessions and process experts for 10 sessions (n = 3). After each of the 21 small group sessions, students evaluated their preceptors' teaching with a standardized instrument. Students' knowledge acquisition was assessed by an end-of-course multiple choice (EOC-MCQ) examination. RESULTS: Students rated the process experts significantly higher on each of the instrument's 15 items, including the overall rating. Students' mean score (±SD) on the EOC-MCQ exam was 76.1% (8.1) for groups taught by content experts, 78.2% (7.8) for the combination group and 79.5% (9.2) for process expert groups (p = 0.11). By linear regression student performance was higher if they had been taught by process experts (regression coefficient 2.7 [0.1, 5.4], p < .05), but not content experts (p = .09). CONCLUSIONS: When preceptors are physicians, content expertise is not a prerequisite to teach first year medical students within a structured small group environment; preceptors with process expertise result in at least equivalent, if not superior, student outcomes in this setting.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".