A critical narrative review of transfer of basic science knowledge in health professions education
Bibliographic record
Abstract
CONTEXT: 'Transfer' is the application of a previously learned concept to solve a new problem in another context. Transfer is essential for basic science education because, to be valuable, basic science knowledge must be transferred to clinical problem solving. Therefore, better understanding of interventions that enhance the transfer of basic science knowledge to clinical reasoning is essential. This review systematically identifies interventions described in the health professions education (HPE) literature that document the transfer of basic science knowledge to clinical reasoning, and considers teaching and assessment strategies. METHODS: A systematic search of the literature was conducted. Articles related to basic science teaching at the undergraduate level in HPE were analysed using a 'transfer out'/'transfer in' conceptual framework. 'Transfer out' refers to the application of knowledge developed in one learning situation to the solving of a new problem. 'Transfer in' refers to the use of previously acquired knowledge to learn from new problems or learning situations. RESULTS: Of 9803 articles initially identified, 627 studies were retrieved for full text evaluation; 15 were included in the literature review. A total of 93% explored 'transfer out' to clinical reasoning and 7% (one article) explored 'transfer in'. Measures of 'transfer out' fostered by basic science knowledge included diagnostic accuracy over time and in new clinical cases. Basic science knowledge supported learning - 'transfer in' - of new related content and ultimately the 'transfer out' to diagnostic reasoning. Successful teaching strategies included the making of connections between basic and clinical sciences, the use of commonsense analogies, and the study of multiple clinical problems in multiple contexts. Performance on recall tests did not reflect the transfer of basic science knowledge to clinical reasoning. CONCLUSIONS: Transfer of basic science knowledge to clinical reasoning is an essential component of HPE that requires further development for implementation and scholarship.
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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 | 0.263 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".