Through the Lens of Societal Norms and Experiences: Students’ Conceptualization of Patient Case Data When Diversity is Apparent
Bibliographic record
Abstract
OBJECTIVE: There are increasing calls to improve the representation of diversity within case-based learning materials, yet it is unclear how students interpret the inclusion of diversity data when synthesizing case information. The objective of this study was to determine factors that influence students' interpretation of written case data for visualization of a patient case. METHODS: This was a qualitative study using interviews. Entry-to-practice pharmacy students from Dalhousie University in Canada were recruited to review 6 cases, each with varying representations of diversity (eg, race, sexual orientation, gender, relationship status, disability, or none). Students were prompted to state how they visualized the case patient and what factors influenced their perceptions. Interviews were audio-recorded and transcribed verbatim. A reflexive thematic analysis was conducted to interpret themes. RESULTS: Interviews were conducted with 18 students. Students relied on 5 factors when interpreting case data in the presence of diversity. In addition to the case data itself, these included personal experience (relating to themselves or personal relationships), professional experience (through work or school), population stereotypes, and perceived societal norms. CONCLUSION: This study found that students rely on their personal and professional experiences, perceptions, and social conditioning when interpreting the presence of diversity within learning materials. Findings support the notion that educators should deliberately and conscientiously expose students to a broad representation of diverse populations to increase students' knowledge and understanding of populations, and to create intentional time and space to challenge existing stereotypes that contribute to the inequities in health care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".