Did we create brave spaces? A realist evaluation report on simulation-based faculty development workshop in equity, diversity, inclusivity, and Indigenous reconciliation
Bibliographic record
Abstract
BACKGROUND: Creating Brave Spaces (CBS) workshops are designed by an interprofessional team of health professions educators to empower faculty members to disrupt microaggressions in the clinical teaching environment using simulation-based education design, where actors were trained to portray sources of microaggressions. METHODS: The CBS team delivered eleven workshops addressing five categories of biases in various contexts during 2020-2024 engaging hundreds of participants. The team recruited participants to conduct semi-structured interviews. Records from team meetings and facilitator focus groups were collected and reviewed. The dataset was subjected to thematic analysis focusing on the participants' experience in the workshop. Themes were presented in Context-Mechanism-Outcome statements informed by the realist evaluation framework. Subsequently, the results were verified with participants. RESULTS: Nine participants volunteered to be interviewed 2 to 12 weeks after attending the workshop. The interview scripts, totaling about 60,000 words, provided a rich picture of faculty members' backgrounds and experiences. Thematic analysis yielded the following results. Simulation-based education design empowered faculty members to overcome barriers and progress in their skills. During the immersive experience, participants benefited from a rare opportunity to practice aligning their values with their actions. Those who experienced microaggressions as victims or passive bystanders in their past experienced heightened emotions. Faculty members agreed that disrupting microaggressions is an important part of their work. They navigated the tension between "calling in" the source of the microaggression, being mindful of power dynamics in the simulated cases, and "calling out" the harm of microaggressions by holding the source accountable. Some recounted successes in managing subsequent incidences of microaggressions in their clinical teaching environment. The results were validated by a member-checking process, and further supported by recorded conversations during team meetings and facilitator focus groups. CONCLUSIONS: Health sciences institutions' stated strategic goals in inclusive excellence, although widely accepted by faculty members, are challenging to operationalize in the moment of a microaggression. Participants practiced this skill using simulation-based education design and reported significant and positive impacts.
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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 | 0.080 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".