Teaching citizenship in an international pharmacy practice course
Bibliographic record
Abstract
Abstract Pharmacy education is focused on preparing students for practice as health professionals. Curriculum for this professional programme includes pharmaceutical, social and health sciences. For pharmacy students to appreciate their role as citizens, exposure to citizenship teaching must be integrated into the curriculum, but this has not been studied within pharmacy. The purpose of our research was to describe the impact of an international pharmacy elective course that placed University of Alberta pharmacy students in Italy for a three-week period. Activities within the course were designed to expose the students to social justice, health policy, citizenship and their role in addressing health issues. Student interviews and course materials were analysed for emerging themes. The analysis identified themes relating to an developing awareness of citizenship and the personal and professional roles the students could play; appreciating broader health issues in the world, and the realization that medications are not always the primary approach for health; the importance of policy and advocacy for the right to food and health; and the integration of professionalism, advocacy, justice and citizenship in addressing health. Student learning regarding citizenship was supported through course activities and the context of the cross-cultural setting. The learnings about citizenship started at a basic level, identifying a significant curriculum gap in pharmacy education. Further collaboration between Pharmacy and Education, and international course experiences can assist in enhancing the citizenship teaching and learning of future health professionals.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".