Effects of high-fidelity simulation on self-efficacy in undergraduate nursing education regarding family systems care and early palliative care
Bibliographic record
Abstract
Objective: Family systems care and palliative care are main topics in nursing education and practice. Self-efficacy of undergraduate nursing students is strengthened by high-fidelity simulation. The aim of this study was to explore the effects of high-fidelity simulation on the self-efficacy of undergraduate nursing students regarding family systems care and early palliative care in an adult setting.Methods: A quasi-experimental study design with repeated measures was conducted. Self-Efficacy was measured using the Family Nursing Practice Scale (FNPS) and the Self-Efficacy-Subscale of the Bonner Palliativwissenstest (BPW) before the start of the theoretical family systems care and palliative care courses (t1), after completion of the courses (t2), immediately after high-fidelity simulation (t3) and 3 months after high-fidelity simulation (t4). A linear mixed model was performed to evaluate the difference of self-efficacy between the times of measurement.Results: A total of 46 undergraduate nursing students participated in the study. There were statistically significant differences regarding the FNPS between t1 and t3 (p = .0019) as well as t1 and t4 (p = .0198), and regarding the BPW between t1 and t3 (p ≤ .0001), t1 and t4 (p = .0012), as well as t2 and t3 (p = .0112). Between the other times of measurement, no statistically significant differences were found.Conclusions: High-fidelity simulation in combination with traditional learning methods can have a short- and long-term effect on undergraduate nursing students’ self-efficacy regarding family systems care and early palliative care in hospitalized adult patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".