Supporting Students in Relation to Racism: Occupational Therapy Fieldwork Educator Perspectives
Bibliographic record
Abstract
Background. Racism is ingrained within the Canadian healthcare system, resulting in health inequities for Black, Indigenous, and people of colour (BIPOC) service users, healthcare workers, and students. Occupational therapy students spend a large amount of their training with fieldwork educators within these environments. Purpose. Explore the experiences of occupational therapy fieldwork educators in relation to supporting students witnessing or experiencing racism during fieldwork education. Method. Using an interpretive description research design, with a semi-structured interview guide, we conducted individual and group interviews with nine occupational therapy fieldwork educators with a minimum of two years of clinical experience. Data analysis included data immersion, independent coding by multiple team members, and grouping and collapsing data to develop categories. Findings. All participant educators discussed racism with students, but had varying levels of comfort doing so. The participants used reflection as a tool to support students learning about racism. Participants were “self-taught” regarding racism and wanted help to develop their own, as well as their students’ skills in responding to racism. Conclusion. Health organizations and universities need to work in an integrated way to prioritize anti-racism education for all occupational therapy students and practitioners, and to develop multipronged systems for the disclosure and addressing of racism.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".