What is ‘disability’? Exploring the conceptions of internationally educated learners in a Canadian physical therapy bridging program
Bibliographic record
Abstract
BACKGROUND: Physiotherapists' understanding of disability has direct relevance for their practices and work with disabled people. PURPOSE: To explore how physiotherapists from diverse backgrounds, cultures and social locations conceptualize disability. Understanding their conceptualizations will provide insight to determine future directions regarding disability education, in healthcare education, to optimize the quality of care and life for individuals with disabilities. METHODS: Six internationally educated physiotherapists enrolled in a Canadian physical therapy bridging program were interviewed and data were analyzed for themes. RESULTS: Participants' conceptualizations of disability were complex and a dynamic process that reflected what was 'thinkable' in relation to experience, such as with social location and in personal, professional and educational domains. This primary theme was reflected in three subthemes: 1) Professional PT acculturation shaped disability conceptualization in particular ways; 2) Medical model as the dominant way of thinking; and 3) Social model thinking was more feasible in resource rich contexts. CONCLUSION: These results reinforce the benefits of a wide exposure to different ways of conceptualizing and addressing disability within PT education to counter the dominance of the medical model and reinforce the importance of advocacy and allyship with disabled people.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".