Inclusion of Health Disparities, Cultural Competence, and Health Literacy Content in US and Canadian Pharmacy Curriculums
Bibliographic record
Abstract
<b>Objective.</b> To determine how US and Canadian pharmacy schools include content related to health disparities and cultural competence and health literacy in curriculum as well as to review assessment practices. <b>Methods.</b> A cross-sectional survey was distributed to 143 accredited and candidate-status pharmacy programs in the United States and 10 in Canada in three phases. Statistical analysis was performed to assess inter-institutional variability and relationships between institutional characteristics and survey results. <b>Results.</b> After stratification by institutional characteristics, no significant differences were found between the 72 (50%) responding institutions in the United States and the eight (80%) in Canada. A core group of faculty typically taught health disparities and cultural competence content and/or health literacy. Health disparities and cultural competence was primarily taught in multiple courses across multiple years in the pre-APPE curriculum. While health literacy was primarily taught in multiple courses in one year in the pre-APPE curriculum in Canada (75.0%), delivery of health literacy was more varied in the United States, including in a single course (20.0%), multiple courses in one year (17.1%), and multiple courses in multiple years (48.6%). Health disparities and cultural competence and health literacy was mostly taught at the introduction or reinforcement level. Active-learning approaches were mostly used in the United States, whereas in Canada active learning was more frequently used in teaching health literacy (62.5%) than health disparities and cultural competence (37.5%). Few institutions reported providing professional preceptor development. <b>Conclusion.</b> The majority of responding pharmacy schools in the United States and Canada include content on health disparities and cultural competence content and health literacy to varying degrees; however, less is required and implemented within experiential programs and the co-curriculum. Opportunities remain to expand and apply information on health disparities and cultural competence content and health literacy content, particularly outside the didactic curriculum, as well as to identify barriers for integration.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 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".