Guiding Ethics Review in Pharmacy Education Research and Scholarship at UBC: Clarifying the Unclear
Bibliographic record
Abstract
While it goes without saying that ethically sound practices are imperative for high-quality educational scholarship, institutional ethics guidance is often unclear about how to treat educational scholarship generally, and quality improvement/assurance studies and the scholarship of teaching and learning, specifically. Amongst health profession education researchers, including those in pharmacy, this lack of clarity has led to confusion regarding existing ethics governance and ambivalence regarding ethics requirements. Drawing on the experiences of one pharmacy school in western Canada, this commentary describes an ethics vetting guide developed explicitly to address current uncertainty about ethics requirements for pharmacy education scholarship. Clarifying the problem, describing the guide, and exploring what was learned along the way provide a basis for re-centering ethics in the development of scholarly projects and decision-making regarding formal ethics review. The importance of instilling ethical intelligence, delineating research from quality improvement/assurance work, and addressing current gaps in ethics oversight and governance of educational scholarship are among key lessons learned during guide development along with suggestions for new institutional ethics guidance directly targeting educational scholarship to supplement current national guidelines.
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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 | 0.534 | 0.548 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.042 | 0.081 |
| Scholarly communication | 0.055 | 0.033 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.044 | 0.063 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".