Reconsidering Basic: Integrating Social and Behavioral Sciences to Support Learning
Bibliographic record
Abstract
PURPOSE: The integration of basic science mechanistic knowledge (pathophysiology and etiology) with clinical features (signs and symptoms) during learning leads to robust cognitive representations in novices and supports the development of clinical reasoning, including better diagnostic accuracy and later learning of related concepts. However, previous studies have used a limited scope of traditional biomedical sciences, including biochemistry, anatomy, and physiology. The use of extended forms of foundational knowledge, including behavioral and sociological sciences, that have been proposed to support learning and performance in complex health systems remains unexplored. METHOD: Thirty-three first-year medical students from the University of Toronto MD Program participated in the study. The effect of integrated extended basic science (EBS) learning was compared with that of clinically focused instruction on an initial assessment of diagnosis using clinical vignettes and a "preparation for future learning" assessment (PFLA) to assess learning of new related content in medical psychiatry (co-occurring physical and mental health conditions). RESULTS: Both forms of instruction supported the development of diagnostic ability on initial assessment (t[30] = 1.20, P = .24). On the PFLA, integrated instruction of extended forms of basic science led to superior performance on assessing complex patients' health care needs (t[30] = 2.70, P < .05). CONCLUSIONS: Similar to previous studies using integration of biomedical sciences, the integration of EBS can enhance later learning of new related concepts. These results have implications for curriculum design to support development of expert clinical reasoning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".