Integration and timing of basic and clinical sciences education
Bibliographic record
Abstract
BACKGROUND: Medical education has traditionally been compartmentalized into basic and clinical sciences, with the latter being viewed as the skillful application of the former. Over time, the relevance of basic sciences has become defined by their role in supporting clinical problem solving rather than being, of themselves, a defining knowledge base of physicians. METHODS: As part of the national Future of Medical Education in Canada (FMEC MD) project, a comprehensive empirical environmental scan identified the timing and integration of basic sciences as a key pressing issue for medical education. Using the literature review, key informant interviews, stakeholder meetings, and subsequent consultation forums from the FMEC project, this paper details the empirical basis for focusing on the role of basic science, the evidentiary foundations for current practices, and the implications for medical education. FINDINGS: Despite a dearth of definitive relevant studies, opinions about how best to integrate the sciences remain strong. Resource allocation, political power, educational philosophy, and the shift from a knowledge-based to a problem-solving profession all influence the debate. There was little disagreement that both sciences are important, that many traditional models emphasized deep understanding of limited basic science disciplines at the expense of other relevant content such as social sciences, or that teaching the sciences contemporaneously rather than sequentially has theoretical and practical merit. Innovations in integrated curriculum design have occurred internationally. Less clear are the appropriate balance of the sciences, the best integration model, and solutions to the political and practical challenges of integrated curricula. DISCUSSION: New curricula tend to emphasize integration, development of more diverse physician competencies, and preparation of physicians to adapt to evolving technology and patients' expectations. Refocusing the basic/clinical dichotomy to a foundational/applied model may yield benefits in training widely competent future physicians.
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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 | 0.035 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".