Well-being Content Inclusion in Pharmacy Education Across the United States and Canada
Bibliographic record
Abstract
Objective. To describe the landscape of Well-Being (WB) content inclusion across schools and colleges of pharmacy (S/COP) in the United States (U.S.) and Canada through identification of content implementation, incorporation, and assessment. Methods. A cross-sectional survey was distributed to all accredited S/COP in the U.S. (n=143) and Canada (n=10). Survey questions included curricular and co-curricular timing, frequency, assessment strategies and support for WB initiatives, using a framework of eight dimensions (pillars) of wellness to categorize content. Results. Descriptive data analyses were applied to 99 completed surveys (65%), 89 (62%) in the U.S. and 10 (100%) in Canada. WB content was most prevalent within the co-curricular realm and incorporated into didactic and elective more than experiential curricula. Most content came from intellectual, emotional, and physical pillars, and least from financial, spiritual, and environmental pillars. Less than 50% of S/COP include WB within their strategic plans or core values. Funding is primarily at the University (59%) or S/COP (59%) levels. Almost half of respondents reported inclusion of some assessment, with a need for more training, expertise, and standardization. Conclusion. Survey results revealed a wide range of implementation and assessment of WB programs across the U.S. and Canada. These results provide a reference point for the state of WB programs that can serve as a call to action and research across the Academy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 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".