Teaching and Learning of Pharmacology in Medical Schools: From Canada to Southeast Asia
Bibliographic record
Abstract
Pharmacology in the traditional medical curriculum has been treated as a discrete”preclinical”discipline indentifying itself distinctly from other preclinical sciences or clinical subjects in its knowledge base as well as learning/teaching instructions.It isusually run in series with other pre-clinical courses(e.g.,anatomy,biochemistry,physiology),but in parallel with other para-clinical courses such as pathology,microbiology and community medicine.Clinical pharmacology was only introduced relativelyrecently and was designed to overcome the perceived deficiency in”preclinical”pharmacology” especially in terms of its therapeutic relevance and application to medicine.In many universities,both preclinical and clinical pharmacology courses co-exist,usually independently and are offered by two separate,sometimes non-interacting Departments of Pharmacology and Clinical Pharmacology.In recent years,problem-based medical curricula have emerged,in varied forms,as a platform in which pharmacology is viewed as an integrated component in a holistic approach to medical education.In this problem-based learning(PBL)model,pharmacology is learned in a student-centered environment,based on a self-directed,clinically relevant and case-oriented approach,usually in a small-group tutorial format.In PBL,pharmacology is learned inconcert with other subject issues relevant to the case-problem in question,such as anatomy, physiology,pathology,microbiology,population health,and behavior science.Achange towards a PBL curriculum appears to be beneficial in better preparing the medicalstudents as life-long learners capable of coping with changes in knowledge and skills associated with the progressive and dynamic social/economic transformation in theAsia-Pacific region.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".