Equity, diversity and inclusion in clinical simulation healthcare education and training: An integrative review
Bibliographic record
Abstract
Patient profiles have changed from shifting demographics, globalization and immigration. Such changes highlight the need to educate and train healthcare trainees and healthcare providers (HCPs) on the provision of person-centred care through an equity, diversity and inclusion (EDI) approach. Simulation pedagogy has the potential to be a useful and impactful teaching and learning approach for EDI. The purpose of this review was to explore and summarize the current literature on the level of integration and state of EDI in clinical simulation within healthcare education, curricula and training. An integrative literature review was conducted using Whittemore and Knafl’s (2005) method. Studies that met the selection criteria were assessed using the Johns Hopkins Nursing Evidence-Based Practice Model. A total of 64 studies were included in the review. Five themes emerged from EDI incorporation in clinical simulation education and training for HCPs and healthcare trainees: (1) increase in self-awareness; (2) enhanced communication; (3) enhanced insight and knowledge; (4) strengthened EDI-related self-efficacy; and (5) increase in EDI-related competence and skills. Clinical simulation provides opportunities for EDI integration in healthcare education. Several implications were identified: (1) employing a more systematic process for EDI integration in healthcare education and programs; (2) developing a digital repository of EDI-focused clinical scenarios; (3) co-creating EDI-focused clinical simulations with persons of diverse background; (4) the importance of maintaining a safe learning environment for all involved persons – learners, staff, faculty and standardized/simulated patients in the EDI simulations; and (5) the need for more robust and rigorous research to advance the science of clinical simulation.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".