Exploring the landscape of pharmacology education in Health Professions Programs: From historical perspectives to current approaches to teaching
Bibliographic record
Abstract
Although health care professionals have been providing care as part of organized medical systems for millennia, therapeutics in its current sense only emerged in the nineteenth century. Teaching was conducted primarily using a lecture-based format. The Therapeutic Revolution of the 1930s heralded an explosion in the number and types of therapies available. As therapy has evolved so has teaching. Didactic teaching has, in many cases, been replaced by active learning and the health professions curriculum has shifted from an instructor-centered and discipline-based to a learner-centered, competence-based model. Pharmacology as a stand-alone discipline has largely been integrated into systems based or other modes of teaching. Assessments have also evolved from traditional examination formats that emphasized rote knowledge memorization to other assessment formats such as objective structured clinical examinations that emphasize evaluation of skills and attitudes. It has been challenging to define the best modalities given the wide variances in health professions education and the structure of health care systems internationally. Nonetheless, International collaboration efforts have been crucial to define core competencies which can then be used to guide curricular development. Challenges facing educators also include teaching ethical conduct of prescribing and how Artificial Intelligence (AI) can be used in both teaching and evaluation, suggesting the need for on-going dialogue, continuing professional development and research in these important areas. • Pharmacology & Therapeutics teaching evolved from lecture to active learning and competence based. • Pharmacology teaching is increasingly integrated into clinical systems and biomedical curricula. • The expansion of knowledge and technology calls for a focus on teaching core concepts and skills. • Effective assessments focus on attitudes-based and clinical-therapeutic skills. • Challenges include teaching ethical prescribing and incorporating AI in healthcare education.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".