Moving towards deep equity, diversity, inclusivity and accessibility in simulation: a call to explore the promises and perils
Bibliographic record
Abstract
Principles and issues of equity, diversity, inclusivity, and accessibility (EDIA) are being explored currently in simulation designs and trainings but with limited depth, often raising more questions than answers. This editorial invites the broader healthcare simulation community to move beyond the superficial to explore more expansively and deeply these issues of EDIA within simulation. Simulation is the very environment and context from which we may confront how existing (power) structures can be dismantled and re-envisioned for more optimal redistribution of participation, power, and benefits. We can use simulation to experiment with variations of these realities, and start exploring consequences of such alternatives to benefit our broader health systems and societies. Simulation uniquely combines opportunities for experience, reflection, application and active experimentation, enabling a ripe ground for this study. In fact, it is the responsibility of simulation educators to take up this challenge, and to engage in meaningful scholarship to understand more about the impact of simulation in exploring EDIA topics. This editorial invites contributions of empirical and theoretical works that advance our collective understanding of EDIA, while also cautioning against complacency. The simulation community is urged to look inwards and also examine its own practices critically, in spite of the uncertainty, vulnerability and risks that this presents.
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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".