Patient safety: nursing students' perspectives and the role of nursing education to provide safe care
Bibliographic record
Abstract
BACKGROUND AND AIM: Nurses as the largest group of healthcare providers are in the best position to improve patient safety. In preparing future nurses, nurse educators have an important role in developing the knowledge, skills and attitudes among nursing students related to patient safety. The aim of this study was to explore Iranian nursing students' perspectives regarding patient safety and the role of nursing education in developing their capabilities to provide safe care. METHODS: A purposeful sampling strategy was used to recruit 17 junior and senior nursing students for qualitative, semi-structured interviews. Content analysis of the interview transcripts was conducted to identify several relevant themes. FINDINGS: Three main themes emerged from the data analysis: 'safety as patient comfort', 'not being knowledgeable or experienced enough' and 'being helped to internalise the principles and values of patient safety'. The third theme consisted of two categories: 'adopting a humanistic approach towards patients' and 'practising conscientiously in the workplace'. CONCLUSION: The present study adds insights on how nursing students understand and may be helped to internalise concepts of patient safety within their practice. Nursing education curriculum designers need to go beyond theoretical concepts of patient safety education and devise strategies to increase the application of safety knowledge and competencies in nursing practice.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".