Assessing Diversity and Cultural Competency Among Canadian Physiotherapists: A Cross-Sectional Survey, Part 1
Bibliographic record
Abstract
Purpose: The aims of this study were to describe cultural diversity among physiotherapists, to understand the challenges to the delivery of physiotherapy to equity-seeking communities, and to measure and assess factors associated with cultural competency. Method: We conducted a cross-sectional survey (57-items) of Canadian physiotherapists that assessed demographics, cultural competency, and challenges in delivery of rehabilitation. We used backwards elimination method to develop our regression models, and cross-validated by estimating shrinkage. Results: Our sample included 808 physiotherapists (female: 77.8%). Most practitioners identified as White/Caucasian, with Indigenous populations significantly under-represented. Physiotherapists exhibited high scores in cultural awareness and sensitivity (mean 6.0/7 [95% CI: 6.0, 6.1]), with lower scores for cultural competency behaviours (mean 4.5/7 [95% CI: 4.4, 4.5]). There was a weak association between gender (men), uncertainty regarding the presence of cultural health disparities, race (White/Caucasian), prior diversity, equity, and inclusion (DEI) training, and DEI attitudes ( R2 = 0.17, p < 0.0001) with cultural awareness and sensitivity. There was also weak association between engagement in prior DEI training, cultural awareness and sensitivity scores, increased years of clinical experience and race (White/Caucasian) with culturally competent behavioural scores ( R2 = 0.13, p < 0.0001). Conclusions: The findings suggest the need to improve current educational structures in DEI and promote recruitment of diverse physiotherapists. This work may help policy makers and educational institutions in developing initiatives for cultural competence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".