Medical education dilemma: How can we best accommodate basic sciences in a curriculum for 21st century medical students?
Bibliographic record
Abstract
Over the years, the medical curriculum has been changed to accommodate a variety of evolving disciplines and an exploding scientific knowledge of the basic sciences to prepare "a competent physician" of the 21st century. Therefore, we must be innovative in our approach of curricular development if we wish to continue to incorporate new basic sciences knowledge in the face of decreasing contact hours to satisfy the buzz word, "integration". Certainly, the challenges are phenomenal. The question how to best integrate basic sciences, is not easy to answer as the objectives of the courses and outcome vary from one medical school to another and the fact is, one size does not fit all. However, if we believe that basic sciences are the language of medicine and foundation of clinical knowledge, then we must resolve this ongoing dilemma by introducing basic sciences through a better alignment in a given curriculum. The purpose of this review is to evaluate different curricular models for their basic sciences content and address their strengths and weaknesses. In addition, we will introduce a spiral design to integrate basic sciences for senior students. Finally, we will provide some insight as to how learning and retention of basic science content can be sustained.
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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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".