Evaluation of online clinical pharmacology curriculum resources for medical students
Bibliographic record
Abstract
AIMS: To identify and evaluate clinical pharmacology (CP) online curricular (e-Learning) resources that are internationally available for medical students. METHODS: Literature searches of Medline, EMBASE and ERIC databases and an online survey of faculty members of international English language medical schools, were used to identify CP e-Learning resources. Resources that were accessible online in English and aimed to improve the quality of prescribing specific medications were then evaluated using a summary percentage score for comprehensiveness, usability and quality, and for content suitability. RESULTS: Our literature searches and survey of 252 faculty (40.7% response rate) in 219 medical schools identified 22 and 59 resources respectively. After screening and removing duplicates, 8 eligible resources remained for evaluation. Mean total score was 53% (standard deviation = 13). The Australian National Prescribing Curriculum, ranked highest with a score of 77%, based primarily on very good ratings for usability, quality and suitable content. CONCLUSION: Using a novel method and evaluation metric to identify, classify, and rate English language CP e-Learning resources, the National Prescribing Curriculum was the highest ranked open access resource. Future work is required to implement and evaluate its effectiveness on prescribing competence.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".