Back from basics: integration of science and practice in medical education
Bibliographic record
Abstract
CONTEXT: In 1988, the Edinburgh Declaration challenged medical teachers, curriculum designers and leaders to make an organised effort to change medical education for the better. Among a series of recommendations was a call to integrate training in science and clinical practice across a breadth of clinical contexts. The aim was to create physicians who could serve the needs of all people and provide care in a multitude of contexts. In the years since, in the numerous efforts towards integration, new models of curricula have been proposed and implemented with varying levels of success. SCOPE OF REVIEW: In this paper, we examine the evolution of curricular integration since the Edinburgh Declaration, and discuss theoretical advances and practical solutions. In doing so, we draw on recent consensus reports on the state of medical education, emblematic initiatives reported in the literature, and developments in education theory pertinent to the role of integrated curricula. CONCLUSIONS: Interest in integration persists despite 30 years of efforts to respond to the Edinburgh Declaration. We argue, however, that a critical shift has taken place with respect to the conception of integration, whereby empirical models support a view of integration as pertaining to both cognitive activity and curricular structure. In addition, we describe a broader definition of 'basic science' relevant to clinical practice that encompasses social and behavioural sciences, as well as knowledge derived from biomedical science.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.046 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".