The Creating Brave Spaces workshop: a report on simulation-based faculty development to disarm microaggressions
Bibliographic record
Abstract
Background Microaggressions occur regularly in the clinical and teaching environments and is harmful to individuals, teams and institutions. The aim of this brief report is to share experiences in developing and conducting a simulation-based faculty development initiative, the Creating Brave Spaces (CBS) workshop, to disarm microaggressions. Methods In 2021–2023, a total of six workshops were arranged for faculty in different settings, including faculty development events, faculty retreats, national and international conferences. From each workshop, the team gained insight and experience that they incorporated into additional deliveries. Experiences and lessons learnt from facilitators have been subject to systematic reflection by the authors. Results A total of 85 faculty participated in the workshops. We experienced that context was important and that participants varied greatly in their understanding of the concept of microaggression. We also found that participants play an active role in the co-creating of the learning experience. Highly engaged participants have shared their own techniques to disarm microaggressions with each other, adding value to the workshop. We experienced that facilitators found it helpful to debrief as a team after each event and incorporate experiences into future deliveries. Conclusion The CBS workshop is a feasible approach to build awareness about microaggressions and to learn strategies to disarm microaggressions.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".