Should we teach using schemas? Evidence from a randomised trial
Bibliographic record
Abstract
CONTEXT: Schema-based instruction may alter knowledge organisation and diagnostic reasoning strategies through the provision of structured knowledge to novice trainees. The effects of schema-based instruction on diagnostic accuracy and knowledge organisation have not been rigorously tested. METHODS: Year 2 medical students were randomised to learn four cardiac diagnoses using schema-based instruction (n = 26) or traditional instruction (n = 27) on a high-fidelity cardiopulmonary simulator (CPS). Students completed case-based learning in groups of two to five and underwent individual written and practical tests. The written test consisted of questions testing features that linked or distinguished diagnoses (structured knowledge) and questions testing features of individual diagnoses (factual knowledge). A practical test of diagnostic accuracy on the CPS was performed for two diagnoses present in the learning phase (taught lesions) and two untaught lesions. A majority of students (n = 37, 70%) voluntarily returned for follow-up written testing 2-4 weeks later. RESULTS: Learning time and accuracy did not differ between students on schema-based and those on traditional instruction. Students receiving schema-based instruction performed better on structured knowledge questions (p < 0.001) and no differently on factual knowledge questions (p = 0.7). Relative differences between groups remained unchanged on follow-up testing. Diagnostic success was higher in the schema-based instruction group for taught lesions (mean difference = 38%, 95% confidence interval [CI] 20-56; p < 0.001) and untaught lesions (mean difference = 31%, 95% CI 15-48; p < 0.001). CONCLUSIONS: Schema-based instruction was associated with improved retention of structured knowledge and diagnostic performance among novices. This study provides important proof-of-concept for a schema-based approach and suggests there is substantial benefit to using this approach with novice trainees.
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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 | 0.001 | 0.333 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".