PEARLS debriefing for social justice and equity: integrating health advocacy in simulation-based education
Bibliographic record
Abstract
Addressing health inequities in health professions education is essential for preparing healthcare workers to meet the demands of diverse communities. While simulation has become a widely recognized and effective method for providing safe and authentic clinical learning experiences, there has been limited attention towards the power of simulation in preparing health practitioners to work with groups who experience health disparities due to systems of inequality. Balancing technical proficiency with educational approaches that foster critical reflection and inform action oriented towards social accountability is essential. Transformational learning promotes the development of critical consciousness through critical reflection. Debriefing plays a crucial role in fostering learning in this direction by providing a structured opportunity to critically reflect on taken for granted assumptions, examine power and privilege embedded within systems and structures, and empower learners to take action toward changing those conditions. Building on the Promoting Excellence and Reflective Learning in Simulation (PEARLS) Healthcare Debriefing Tool, we propose a PEARLS Debriefing for Social Justice and Equity (DSJE) Tool that specifically directs attention to systems of inequality that contribute to health disparities for vulnerable groups across a range of simulation scenarios. This approach has two aims: (a) to transform debriefings into a critically reflective space by engaging learners in dialogue about social and structural determinants of health that may create or perpetuate inequities and (b) to foster critical reflection on what actions can be taken to improve the health and well-being of identified at risk and vulnerable groups. From this perspective, we can use the adapted PEARLS Tool to incorporate conversations about systems of inequality, equity, diversity, and inclusion (EDI) into our existing educational practices, and make concentrated efforts towards community-driven and socially conscious simulation-based education (SBE).
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 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 | 0.049 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".