Impact of Standardised Patient Simulation Training on Clinical Competence, Knowledge, and Attitudes in Mental Health Nursing Education
Bibliographic record
Abstract
BACKGROUND: The limited practical placement opportunities in mental health care often induce uncertainty among nursing students. To ameliorate this, simulation training, especially with standardized patients (SPs), is employed to promote clinical competence, allowing students to navigate the complexities associated with mental health nursing, including stigma and stereotypes. OBJECTIVE: This systematic literature review primarily aims to explore and synthesise the studies in simulation education research conducted related to the effects of SPs on clinical competence, knowledge and attitudes of undergraduate pre-registration mental health nursing students. METHODS: following the systematic literature review approach, a comprehensive search was conducted across five electronic databases: MEDLINE, CINAHL, Embase, PsycINFO, and Scopus. The PICO model guided the identification of search terms. The Mixed Methods Appraisal Tool (MMAT) evaluated study quality. RESULTS: Ten studies were included, all examining the impact of SP simulations on undergraduate nursing students. Of these, five evaluated confidence and anxiety levels, while two assessed competence and satisfaction. Other aspects such as motivation, preparation, knowledge, communication skills, and critical thinking were examined individually. The collective results indicate SP simulation as a potentially efficacious strategy for enhancing competencies in graduate nursing education. CONCLUSION: Across all studies, SPs in simulation methods exerted a positive influence on mental health nursing education, bolstering students' preparation for clinical practice by reducing anxiety and fostering confidence, competence, knowledge, and communication skills. However, limitations including insufficient supervision, small sample sizes, homogenous samples, and absence of control groups were present in all studies. Future research should address these issues to fortify evidence supporting the use of SPs in mental health nursing education. RECOMMENDATIONS: Further robust, experimental research with larger sample sizes and validated assessment tools is needed to corroborate these findings and explore the effects of SP simulations on a wider array of learning outcomes.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".