Equity-focused teaching goals in health professions education: an analysis of teaching cases in a Master’s of Physical Therapy program
Bibliographic record
Abstract
Purpose This study explores the representation of socio-demographic characteristics and social determinants of health (SDOH) within teaching cases in a professional Master of Physical Therapy program, assessing their potential to promote the development of effective approaches to equity-focused physical therapy care among students.Methods Using an intersectionality framework and the SDOH model, 74 teaching cases from a Canadian Master of Science in Physical Therapy program were systematically analysed. Cases were evaluated for explicit inclusion of socio-demographic characteristics (e.g. race, gender identity, sexual orientation) and SDOH elements (e.g. income, social support, physical environment). Data were summarised to identify patterns and frequencies using summary statistics.Results The analysis revealed significant gaps: race was mentioned in only 9.45% of cases, gender identity in 2.70%, Indigenous identities in 1.35%, and sexual orientation was absent. Socio-economic status (56.75%) and sex (66.21%) were more frequently included. Among SDOH, personal health practices (78.37%) and physical environment (70.27%) were commonly referenced, whereas culture (2.70%) and gender (18.91%) were underrepresented. Many cases prioritised clinical details over structural barriers, limiting opportunities to develop structural competence.Conclusion Teaching cases inadequately represent key socio-demographic and SDOH factors, undermining their potential to foster equity-oriented clinical reasoning and structural competence. The study recommends standardised, equity-focused case frameworks to strengthen anti-oppressive 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".