Opinions and Perspectives of Canadian Occupational Therapists on Artificial Intelligence
Bibliographic record
Abstract
Background: Technology is rapidly being developed to improve healthcare outcomes. However, the attitudes and perceptions of occupational therapists (OTs) on artificial intelligence (AI) in healthcare are not yet known. Purpose: This study aims to: explore Canadian OTs’ (a) understanding and knowledge on AI, (b) opinions and perspectives on AI, and (c) perceptions of potential benefits and risks AI might bring to occupational therapy practice in Canada. Method: A sequential explanatory mixed method approach was used to gather perspectives of Canadian registered OTs. Two hundred and eighty-two survey respondents and 15 focus group participants took part in the study. Findings: Three main themes emerged: “AI Knowledge and Implementation,” “Use of AI in Occupational Therapy,” and “Human vs. Machine.” OTs have various levels of understanding of AI, and its capabilities within practice and are open to AI use in practice. Although ethical concerns must be addressed, OTs do not perceive AI to pose a threat to employment. Conclusion: OTs have the ability to implement and guide policy changes for technology adoption, and understanding their current perspectives creates opportunities to advocate for change in the field. Further education is needed to better prepare professionals for clinical usage of AI.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".