Knowledge Gaps Regarding Indigenous Health in Occupational Therapy: A National Survey
Bibliographic record
Abstract
Background. There is a need for the occupational therapy profession to respond to the Truth and Reconciliation Commission of Canada Calls to Action and work towards supporting the health and well-being of Indigenous Peoples. Purpose. (1) To determine the knowledge gaps of occupational therapists about Indigenous health and (2) to create recommendations to address identified gaps and inform responses from the profession. Method. A national needs survey was created and distributed to occupational therapists across Canada to determine the knowledge of occupational therapists about Indigenous health. Survey results were analyzed using thematic analysis and descriptive statistics. Findings. Data collected from 364 survey responses informed six distinct themes representing knowledge gaps of occupational therapists related to Indigenous health as follows: lack of foundational knowledge, power relations, lifelong learner, need for appropriate tools/approaches, respectful collaboration, and environmental influences. Implications. The project offers insight into the role of the occupational therapy profession in the process of reconciliation. Insights are focused on decolonizing occupational therapy practice, building trusting relationships with Indigenous Peoples, and the provision of appropriate training for occupational therapists to engage in culturally safer practices.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".