Problem-based Learning and the Teaching of Pharmacology
Bibliographic record
Abstract
Problem-based learning (PBL) is a novel and innovative medical education paradigm which has existed for more than thirty years, since it was first introduced in the late 1960s by McMaster University in Canada. Its student-driven self-directed learning approach makes it distinctly different from the traditional teacher-driven approach to leaning. The phenomenal growth in medical knowledge and information of the past century has served as a wake-up call for all medical educators, to the need for newer methods of training the doctor of tomorrow who must be equipped with special skills and attitudes in order to survive the new order. The PBL approach is increasingly the popular choice for this purpose. However, there is still a lot of anxiety expressed, especially by medical teachers in institutions that are planning a transition from the traditional to the PBL curriculum. One such major concern is the fear that the PBL approach may not be as efficacious as the traditional in imparting subject content to students. The authors share their years of experience in the teaching of Pharmacology in the traditional curriculum and while extolling its achievements concur that the PBL approach is innovative and if well managed could effectively deliver the pharmacology component of a medical curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".