Justice in Educational Content: A Guide to Racial and Cultural Representation in Academic and Clinical Teaching and Assessment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Case-based instruction is broadly used in health professions education, including physical therapy education. Case-based instruction can support achievement of higher-order, applied, learning objectives and clinical reasoning. Instructors strive to represent the diversity of the clinical population in case studies and may have explicit intercultural competency objectives. The inclusion of cultural, racial, and ethnic characteristics in cases or assessments can potentially reinforce stereotypes or inaccurately emphasize these characteristics as direct predictors of health profile. Furthermore, as most physical therapy faculty creating cases are from a White majority stance, there is a risk that inclusion of cultural elements risks inappropriate and biased representation. POSITION AND RATIONALE: Well-intentioned instructors risk substituting cultural, racial, and ethnic characteristics for social and structural determinants of health. Race is a social, not biologic construction and should not be confused. Informed instructors guided by evidence-based strategies can achieve rich case depictions that do not convey inaccurate risk or alienate learners. DISCUSSION AND CONCLUSION: A curriculum design strategy is offered for case development that brings explicit attention to representation of race and culture. This tool serves as a self-reflective and improvement tool. Continued community and student engagement is necessary to achieve high-quality and instructive case studies.
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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 | 0.045 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".