Bibliographic record
Abstract
Problem-based learning (PBL) replicates life experiences to stimulate learning, the integration of knowledge, and lifelong learning skills, all of which are requirements for veterinary medical education. As the curricular content of veterinary schools expands to immense proportions following advances in medical knowledge and biotechnology, it becomes impracticable to ensure that all students at the beginning of their careers have such a wide knowledge base. Students who are faced with vast amounts of information to learn by rote, much of which may seem irrelevant to their prospective career, may become disillusioned with their chosen course, hence the temptation to convert to a PBL curriculum. The PBL strategy of teaching is becoming increasingly popular in veterinary faculties worldwide, encompassing both curriculum content and a process of learning. In PBL, clinical cases are carefully selected to provoke deep student learning by the acquisition of both basic scientific and clinical knowledge critical to the case; cultivate problem-solving abilities; and encourage the development of team-building, self-directed learning, communication, and self- and peer-assessment skills. Problem-solving skills, understanding of the basic sciences, and clinical performance are all improved by the PBL process. The aim of this paper is to review a decade of literature pertaining to the inclusion of PBL in veterinary and medical curricula.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".