Representation of diversity within written patient cases: Exploring the presence of a “hidden curriculum”
Bibliographic record
Abstract
Abstract Background There are urgent calls to dismantle systemic racism and discrimination in pharmacy education and practice. It is recommended that educational programs critically review learning materials for occurrences of stigmatization that may reinforce population biases and stereotypes. Previous studies, as well as premises from race and queer theories, suggest that written cases that do not appropriately include or acknowledge patient diversity may promote a “hidden curriculum” that may foster implicit biases in student healthcare professionals. Objectives The purpose of this study was to explore the existence of a “hidden curriculum” within case‐based learning materials by determining the extent to which underrepresented populations were represented in case descriptions and how representation occurred within groups. Methods This was a qualitative content analysis of written patient cases (n = 76) used throughout the problem‐based learning curriculum at the College of Pharmacy, Dalhousie University (Halifax, Nova Scotia, Canada). Proportions were calculated for each variable identified to represent the categories of interest (race, gender, sexual orientation, relationship status, and presence of disabilities). Quantified data were analyzed and themes were identified to represent each category assessed. Results Data across all categories were mostly “undefined.” The most defined variables within each category were: white (race, 17.1%), female (gender, 53.9%), heterosexual (sexual orientation, 35.2%), married (relationship status, 29.6%), and wheelchair (disability, 1.3%). Defined variables were representative of dominant cultural groups with little representation of underrepresented populations. Themes identified were undifferentiated (race), binary (gender), heteronormative (sexual orientation), traditional (relationship status), and absent (disabilities). Conclusion Findings support the notion that there may be a “hidden curriculum” reinforcing biases and stereotypes due to the undefined nature of the cases. Educators should prioritize inclusion of diversity within cases as part of a coordinated plan, in order to ensure representation is appropriate and well‐distributed across the curriculum.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".