Post-market drug evaluation research training capacity in Canada: an environmental scan of Canadian educational institutions.
Bibliographic record
Abstract
BACKGROUND: Ongoing efforts by Health Canada intended to modernize the legislation and regulation of pharmaceuticals will help improve the safety and effectiveness of drug products. It will be imperative to ensure that comprehensive and specialized training sites are available to train researchers to support the regulation of therapeutic products. OBJECTIVES: The objective of this educational institution inventory was to conduct an environmental scan of educational institutions in Canada able to train students in areas of post-market drug evaluation research. METHODS: A systematic web-based environmental scan of Canadian institutions was conducted. The website of each university was examined for potential academic programs. Six core programmatic areas were determined a priori as necessary to train competent post-market drug evaluation researchers. These included biostatistics, epidemiology, pharmacoepidemiology, health economics or pharmacoeconomics, pharmacogenetics or pharmacogenomics and patient safety/pharmacovigilance. RESULTS: Twenty-three academic institutions were identified that had the potential to train students in post-market drug evaluation research. Overall, 23 institutions taught courses in epidemiology, 22 in biostatistics, 17 in health economics/pharmacoeconomics, 5 in pharmacoepidemiology, 5 in pharmacogenetics/pharmacogenomics, and 3 in patient safety/pharmacovigilance. Of the 23 institutions, only the University of Ottawa offered six core courses. Two institutions offered five, seven offered four and the remaining 14 offered three or fewer. It is clear that some institutions may offer programs not entirely reflected in the nomenclature used for this review. CONCLUSIONS: As Heath Canada moves towards a more progressive licensing framework, augmented training to increase research capacity and expertise in drug safety and effectiveness is timely and necessary.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".