Use of high-fidelity simulation in advancing palliative care skills in nursing students: A convergent mixed methods study
Bibliographic record
Abstract
Objective: High-fidelity simulation (HFS) has positive effects on different learning outcomes in nursing education. The aim of the study was to develop a comprehensive understanding of the added value of HFS building on traditional learning methods in the development of self-efficacy in Bachelor of Science in Nursing students caring for adult patients and their families in early palliative situations.Methods: A convergent mixed methods study was conducted. In the quantitative study section, a quasi-experimental, repeated measures design was applied measuring self-efficacy using the Self-Efficacy-Subscale of the Bonner Palliativwissenstest (BPW) and the Family Nursing Practice Scale (FNPS). In the qualitative study section, a qualitative descriptive study design was applied. Mixed methods meta-inferences were generated by a joint display table.Results: The added value of HFS concerning strengthening nursing student’s self-efficacy in early palliative care and family systems care was confirmed. The expanded findings were the strengths of HFS with the possibility for students to reflect on their performance and synthesize new insights, as well as the importance of students’ practical experience to integrate family systems care in symptom management.Conclusions: HFS strengthens students in their future role as nurses caring for adult patients and their families in early palliative 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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".