Self-care education across Canadian pharmacy schools: Curriculum survey findings
Bibliographic record
Abstract
Background: Self-care instruction in pharmacy curricula is essential given the impact pharmacists have in caring for patients in the community and their evolving role in this area. The primary objective of this study was to strengthen our current understanding of self-care education across undergraduate Canadian pharmacy programs. Methods: A national curriculum survey and follow-up phone interview was conducted in 2019 to assess the quantity and quality of self-care instruction across Canadian pharmacy schools. Representatives were selected based on theirparticipation in the Association of Faculties of Pharmacy of Canada Self-Care Therapeutics and Minor Ailments special interest group. Results: Responses were received from all 10 pharmacy schools in Canada. Self-care education varies across Canadian pharmacy curricula, reflecting differences in scopes of practice across provinces, topics of interest and availability of space within curricula by the various faculties. Specifically, there was considerable variability in the number of hours devoted to self-care education, course content and methods for integration and teaching. Conclusions: Results of this study may help inform and evolve self-care curricula across the country. We argue that strategies for enhancing current programs may include establishing a minimum number of core hours and topics, expanding natural health product content and curricular content oversight by a lead faculty member. Can Pharm J (Ott) 2021;154:xx-xx.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".