Perspectives of Racialized Physiotherapists in Canada on Their Experiences with Racism in the Physiotherapy Profession
Bibliographic record
Abstract
Purpose: We explored the perspectives of racialized physiotherapists in Canada on their experiences of racism in their roles as physiotherapists. Method: This qualitative descriptive cross-sectional study used semi-structured, one-on-one interviews. Data were organized using NVivo qualitative analysis software and analyzed using inductive and deductive coding following the six-step DEPICT method. Results: Twelve Canadian licenced physiotherapists (four men and eight women, three rural and nine urban, from multiple racialized groups) described the experiences of racism they faced in their roles as physiotherapists at the institutionalized, personally mediated, and internalized levels. These experiences were shaped by their personal characteristics, including accent, geographical location, and country of physiotherapy (PT) education. Participants described their responses to these incidents and provided insight into how the profession can mitigate racism and promote diversity and inclusion. Conclusions: Participants described interpersonal racism often mediated by location and accent and experiences of internalized racism causing self-doubt, but they most commonly detailed institutionalized racism. PT was experienced as being infused with Whiteness, which participants typically responded to by downplaying or ignoring. The findings from this study can be used to stimulate conversations in the Canadian PT community, especially among those in leadership positions, about not only acknowledging racism as an issue but also taking action against it with further research, advocacy, and training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".