The use of psychiatry‐focused simulation in undergraduate nursing education: A systematic search and review
Bibliographic record
Abstract
Evidence on the use of simulation to teach psychiatry and mental health (including addiction) content is emerging, yet no summary of the implementation processes or associated outcomes exists. The aim of this study was to systematically search and review empirical literature on the use of psychiatry-focused simulation in undergraduate nursing education. Objectives were to (i) assess the methodological quality of existing evidence on the use of simulation to teach mental health content to undergraduate nursing students, (ii) describe the operationalization of the simulations, and (iii) summarize the associated quantitative and qualitative outcomes. We conducted online database (MEDLINE, Embase, ERIC, CINAHL, PsycINFO from January 2004 to October 2015) and grey literature searches. Thirty-two simulation studies were identified describing and evaluating six types of simulations (standardized patients, audio simulations, high-fidelity simulators, virtual world, multimodal, and tabletop). Overall, 2724 participants were included in the studies. Studies reflected a limited number of intervention designs, and outcomes were evaluated with qualitative and quantitative methods incorporating a variety of tools. Results indicated that simulation was effective in reducing student anxiety and improving their knowledge, empathy, communication, and confidence. The summarized qualitative findings all supported the benefit of simulation; however, more research is needed to assess the comparative effectiveness of the types of simulations. Recommendations from the findings include the development of guidelines for educators to deliver each simulation component (briefing, active simulation, debriefing). Finally, consensus around appropriate training of facilitators is needed, as is consistent and agreed upon simulation terminology.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".