Did We Create Brave Spaces? A study protocol of a realist evaluation project in health professions education faculty development
Bibliographic record
Abstract
The Creating Brave Spaces (CBS) project was born out of the urgent need to design and implement educational activities for health professions faculty members to engage with and lead in difficult conversations in equity, diversity, inclusivity and Indigenous reconciliation (EDIIR) in the clinical teaching environment. While most faculty members are well aware of the harm of prejudice, many are unequipped to act when faced with microaggressions. Faculty members are increasingly aware of this learning need while they have few learning opportunities to practice the skills. Leveraging simulation-based education design, we created faculty development activities that place faculty members in the “hot seat” during a microaggression incident. While supported by institutional pilot grants, we have received initial positive responses from faculty members. However, simulation-based education is costly due to recruitment and training of actors. Moreover, a successful curriculum in this challenging subject requires careful planning and expert facilitation that would be unsustainable without continuous institutional support. We recognize it is important that we conduct careful program evaluation to continuously iterate and engage. We present here a program evaluation study protocol using a Realist Evaluation framework through thematic analysis. Why did the participants attend this activity? How were they changed? And what elements in the experience affected those changes? These are the questions we aim to answer. While we have yet to complete our analysis, by sharing this protocol, we offer a roadmap for other faculty developers and education scientists to do this work. Our team strives to add voice to innovative faculty development projects that work towards institutional inclusive excellence at the level of everyday interpersonal interactions. When empowered, many faculty members are ready to lead this work in their own context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".