Reconciliation through remote engagement: Evaluation of an online model for indigenous health education in pharmacy
Bibliographic record
Abstract
BACKGROUND: In response to the Truth and Reconciliation Commission of Canada's Calls to Action, pharmacy programs have expanded Indigenous health and cultural safety training. Community-engaged learning is recognized as an effective approach to support this goal, yet such initiatives can be difficult to sustain due to limited faculty and community resources. EDUCATIONAL ACTIVITY: This report outlines the outcomes of a third-year elective pharmacy course on Indigenous cultural safety which developed online community-engaged projects with three remote Indigenous communities. All communication and project activities were conducted remotely via video-conferencing, email, and phone. A community-based participatory action research (CB-PAR) framework was co-developed with Indigenous partners to guide the development of the course, which included project scoping, student onboarding, delivery, and evaluation. EVALUATION OF FINDINGS: Student performance and project impact were assessed using pre/post surveys and semi-structured interviews, with qualitative data analyzed inductively. Seventeen students and six community partners participated in the course evaluation. Two main impacts for students were identified as: (1) a shift in understanding the value of Indigenous perspectives, and (2) enhanced application of cultural safety. Community partners reported positive experiences and emphasized the importance of participating in student assessment and project planning. ANALYSIS OF EDUCATIONAL ACTIVITY: This model of Indigenous pharmacy education demonstrates that impactful, culturally meaningful learning can be achieved via remote learning. It offers a sustainable, scalable approach to engage with geographically remote communities while supporting meaningful Indigenous reconciliation in healthcare education.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".