The Perceived Role of Occupational Therapists in Climate Change
Bibliographic record
Abstract
Background. In 2022, the World Health Organization (WHO) predicted that climate change would cause thousands of additional deaths per year from malnutrition, malaria, diarrhea, and heat stress alone between the years of 2030 and 2050. With such health consequences and environmental changes, climate change is impacting human occupations globally. However, there is a gap in the literature regarding the occupational therapists’ role in climate change, particularly in the Canadian context. Purpose. Our research aimed to explore what is the perceived role of occupational therapists in climate change and climate action from the perspective of Canadian occupational therapists and international experts. Method. This qualitative study used interpretive description methodology. We recruited 12 occupational therapists, including 4 research experts in the field. We conducted semi-structured interviews with each participant. Data were analyzed thematically. Findings. This study uncovered three themes that focused on the complex interconnections between climate challenges and climate actions that occupational therapists are wrestling with personally, clinically, and professionally. Specifically, this study emphasized the importance of supporting individual occupational therapists with their personal challenges, integrating climate actions into clinical practices, and incorporating climate change and climate justice into occupational therapy curricula and professional advocacy. Implications. The environment, including the planet's ecosystem, is a fundamental component in many models of occupational therapy practice. This research provides a rich understanding in the themes of occupational therapists’ perceptions of climate change and climate actions, particularly within a Canadian context.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".