Addressing racism in the workplace through simulation: So much to unlearn
Bibliographic record
Abstract
Introduction Racism exists in the healthcare system and is a root cause of health inequities among Indigenous Peoples. When microaggressions of racism are carried out by healthcare providers, therapeutic trust may be broken and quality of care may be impacted. Anti-racism response training is considered best practice in recognizing and addressing racism. The objective of this study was to evaluate the impact of a virtual (synchronous) anti-racism response training workshop among a group of rehabilitation therapists from across Canada. Methods A 90-minute virtual anti-racism simulation workshop for rehabilitation therapists was developed and delivered virtually four times across Canada between 2020 and 2021. Following an introduction and pre-briefing, role-playing among participants was used to address microaggressive Indigenous-specific racism, followed by an in-depth debriefing with trained facilitators. A post-workshop survey was conducted to evaluate this anti-racism simulation workshop and assess the impact on participating occupational therapists (OTs) and physiotherapists (PTs). Following each simulation workshop, participants were invited to complete an anonymous post-activity survey (n = 20; 50% OTs, 45% PTs). Open text responses were analyzed thematically from the perspective of critical race theory. Results The majority of the participants self-identified as women (95%); white (90%); mid-career (52%); and had never personally experienced racism (70%). All participants agreed that the workshop gave them ideas on how to start dismantling racism in their workplace. Thematic analysis resulted in four themes: so much to unlearn, remain humble, resist the silence, and discomfort is okay. Discussion Despite feelings of discomfort, OTs and PTs appreciated anti-racism skills-based training and recognized the importance of taking action on racism in the workplace. Findings from this study support online (synchronous) anti-racism training as a viable and effective means of creating space for rehabilitation professionals to lean into brave conversations that are necessary for developing strategies to address racial microaggressions impacting Indigenous persons in the workplace. We believe that these small steps of preparing and practicing anti-racism strategies among rehabilitation therapists are essential to achieving a collective goal of dismantling racism in the health system.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".