Developing Research-Informed Guidance on Preparing Pharmacy Students to Care for Diverse Populations
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to develop research-informed guidance on how to better prepare students for working with diverse populations through exposure to diversity representation within case-based learning materials. METHODS: This was a qualitative interpretive phenomenological study using audio-recorded semi-structured interviews for data collection. Interviews were conducted virtually with 15 recent program alumni from Dalhousie University and 15 members from underrepresented communities in Nova Scotia, Canada. Audio-recordings were transcribed verbatim and framework analysis was used to code and categorize data. Themes were interpreted from categorized data and a conceptual model was developed based on the results. RESULTS: The conceptual model highlighted that awareness of diversity and health equity paired with practice and application of learning were perceived to be important for preparing graduates for practice. It was found that awareness could be best achieved through exposure to diversity within cases. To effectively expose students, programs must deliberately identify diverse populations to include, seek perspectives and engagement from those populations when writing cases, ensure conscientious representation of diversity without reinforcing stereotypes, and provide resources for discussion and further learning. CONCLUSION: Through the development of a conceptual model, this study provided research-informed guidance representing diversity within case-based learning materials. Findings support the notion that representation of diversity must be deliberate, conscientious, and collaborative with those offering diverse perspectives and lived experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".