Teaching of Human Physiology as It Relates to Changes in Medical Education:An Asian Perspective Based on the Yin-Yang Principle
Bibliographic record
Abstract
From a clinical perspective, the understanding of human health is evolving with not only the continuing expansion and emergence of new knowledge in bio-logical sciences that are relevant to medicine, but also with the ever increasingly complexity of the environment that affects people's health, including the political, social and economic factors which govern the population and behavioral perspectives of medicine. Conceivably, the concept of human health/illnesses and the art/science of healing may also change in response to the environmental factors that are evolving with the progression of technology and human civilization. It is inevitable that the approach and methods in medical education and the teaching of biological sciences, such as physiology, will also need to adapt to the changes. Incremental, reactive and cosmetic changes have taken place in continuum for the past many decades, usually from the perspective of reductionism. Although a fundamental, proactive and profound change in medical education, emerged as problem-based learning based on cognitive constructivism, was pioneered by McMaster University in Canada more than 3 decades ago, this innovative change has been widely, albeit slowly, adopted around the globe, sometimes in modified (and often distorted)format. Medical schools in many Asian countries have collectively recognized the deficiency of the current traditional medical education in handling the impact of global and regional social-economic changes on the local health care system, and are responding to it in by learning and experimenting innovative approach in medical education. Questions have been asked: ”Can Asian think?” ”Can Asian students do PBL?” Is PBL compatible with Asian culture?
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".