Occupational Therapists’ Experiences of Assessments With Indigenous Peoples: A Storytelling Approach
Bibliographic record
Abstract
Background. Occupational therapists have a responsibility to strive for culturally safer assessments with Indigenous Peoples. Purpose. Explore occupational therapists’ approaches related to culturally safer assessment strategies with Indigenous Peoples, including their perspectives, recommendations, and challenges. Method. Occupational therapists working with Indigenous Peoples across Canada were invited to participate in online surveys and virtual storytelling groups. Data was analyzed with descriptive statistics and thematic analysis. Member checking and collaboration with Indigenous project members consolidated final themes. Findings. Forty-three participants completed surveys and 16 participated in storytelling groups, with three distinct themes emerging: importance of building relationships, the complex nature of obtaining consent, and how systemic barriers negatively affect occupational therapists’ capacity to provide culturally safer assessments. Findings are presented in a composite conversation between an occupational therapist and an Indigenous Knowledge Keeper. Conclusion. Despite systemic pressures towards efficiency and standardized approaches, occupational therapists are attempting efforts towards culturally safer assessments by advocating for occupation-based, culturally relevant, and flexible assessment processes that respect the autonomy of Indigenous service recipients. Future research could explore the current state of curricula related to assessment practices with Indigenous Peoples in Canadian entry to practice occupational therapy programs and perspectives from Indigenous occupational therapists and service recipients.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".