Cognition Before Curriculum
Bibliographic record
Abstract
PURPOSE: Integrating basic science and clinical concepts in the undergraduate medical curriculum is an important challenge for medical education. The health professions education literature includes a variety of educational strategies for integrating basic science and clinical concepts at multiple levels of the curriculum. To date, assessment of this literature has been limited. METHOD: In this critical narrative review, the authors analyzed literature published in the last 30 years (1982-2012) using a previously published integration framework. They included studies that documented approaches to integration at the level of programs, courses, or teaching sessions and that aimed to improve learning outcomes. The authors evaluated these studies for evidence of successful integration and to identify factors that contribute to integration. RESULTS: Several strategies at the program and course level are well described but poorly evaluated. Multiple factors contribute to successful learning, so identifying how interventions at these levels result in successful integration is difficult. Evidence from session-level interventions and experimental studies suggests that integration can be achieved if learning interventions attempt to link basic and clinical science in a causal relationship. These interventions attend to how learners connect different domains of knowledge and suggest that successful integration requires learners to build cognitive associations between basic and clinical science. CONCLUSIONS: One way of understanding the integration of basic and clinical science is as a cognitive activity occurring within learners. This perspective suggests that learner-centered, content-focused, and session-level-oriented strategies can achieve cognitive integration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".