The contribution of high‐fidelity simulation to nursing students' confidence and competence: a systematic review
Bibliographic record
Abstract
YUAN H.B., WILLIAMS B.A. & FANG J.B. (2011) The contribution of high‐fidelity simulation to nursing students' confidence and competence: a systematic review. International Nursing Review 59 , 26–33 Background: High‐fidelity simulation (HFS) has been proposed as a novel, supplemental teaching‐learning strategy to enhance students' confidence and competence in nursing practice. Aim: To describe available evidence about the effects of HFS on students' confidence and competence within nursing educational programmes. Methods: A review of studies published between 2000 and 2011 was undertaken using the following databases: CINAHL, Proquest, MEDLINE, Science Direct, OVID and Chinese Academic Journal. The concepts of confidence and competence as they related to HFS in nursing education were used for screening the literature. Quantitative studies were assessed for methodological quality. Findings: Eighteen English and six Chinese studies addressed confidence and competence as outcomes of simulation and were retrieved in this review. The results of meta‐analysis indicated a mixed contribution of HFS to confidence and competency with a lack of high‐quality random control trials and large sample sizes. Conclusions: Although qualitative studies presented positive results, there was still insufficient evidence for supporting the notion that students' confidence and competency are enhanced through HFS. More quantitative studies are needed to demonstrate effectiveness. There was a deficit of formal measurement tools available to evaluate HFS. Most research pays no attention to validation of measurements. The increased confidence and competence after simulation may not be realized until the student experiences a real situation like the one in the simulation. More research is needed to examine the transferability of the simulation experience into real situations.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".