Integrative learning of physiology and other biomedical sciences organized around primary care cases with healthy patients
Bibliographic record
Abstract
Many health professional school curriculum planners struggle with the problem of using clinical cases for teaching and learning normal physiology and other biomedical sciences. Such cases traditionally involve patients who are ill and are seen by their physicians for diagnosis and treatment of diseases. This tends to focus learning on pathophysiology and management of disease rather than on healthy structure and function, resulting in normal physiology, anatomy, and biochemistry receiving short shrift. An alternative approach is to use clinical scenarios, called “presenting features” cases, in which healthy individuals see their family physicians for preventive care, check‐ups, medical certificates, education, or advice. In this way, physiology is linked with health promotion. After students have learned about normal structure and function, cases can be elaborated to encompass interruptions in health due to patients' deleterious behaviors or to external circumstances, creating a natural continuum from normal physiology to pathophysiology. Here disease prevention is emphasized and biomedical sciences are naturally integrated with social sciences, epidemiology, and other public health topics. This development of cases is founded on the constructivism theory of learning, and authentically reflects the reality of primary health care.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".