Assessing Cultural Competence Among Canadian Physiotherapists: A Qualitative Analysis of a Cross-Sectional Survey, Part 2
Bibliographic record
Abstract
Purpose: This study is the second part of a cross-sectional questionnaire aiming to identify Canadian physiotherapists’ needs and strategies for physiotherapy associations to improve cultural competence in physiotherapy. Method: We conducted a descriptive qualitative analysis of one open-ended question of a cross-sectional questionnaire. We used inductive thematic analysis to develop and modify codes as concepts emerged. Inductive analysis was used to develop the codebook from the qualitative finding of the research study. Results: We received a total of 806 responses, and of those individuals, 485 provided one to three suggestions as part of the open-ended question. We identified two major themes from our open-ended question: (1) education and (2) institutional change. There were four subthemes categorized under education: (1) diversity, equity, inclusion (DEI) training opportunities, (2) resources, (3) accessibility to education, and (4) representation within educational resources. Institutional change was categorized into three subthemes: (1) advocacy, (2) support, and (3) representation in leadership. Conclusions: The results of this questionnaire act as a meaningful and necessary call to action to key stakeholders in the physiotherapy profession. There is a need for educational institutions, regulatory bodies, and associations to re-evaluate policies surrounding cultural competence to improve the delivery of culturally safe health care.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".