Creating an improvement culture for enhanced patient safety: service improvement learning in pre-registration education
Bibliographic record
Abstract
AIM: The present study reports a descriptive survey of nursing students' experience of service improvement learning in the university and practice setting. BACKGROUND: Opportunities to develop service improvement capabilities were embedded into pre-registration programmes at a university in the Northwest of England to ensure future nurses have key skills for the workplace. METHODS: A cross-sectional survey designed to capture key aspects of students' experience was completed by nursing students (n = 148) who had undertaken a service improvement project in the practice setting. RESULTS: Work organizations in which a service improvement project was undertaken were receptive to students' efforts. Students reported increased confidence to undertake service improvement and service improvement capabilities were perceived to be important to future career development and employment prospects. CONCLUSION: Service improvement learning in pre-registration education appears to be acceptable, effective and valued by students. Further research to identify the impact upon future professional practice and patient outcomes would enhance understanding of this developing area. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse Managers can play an active role in creating a service culture in which innovation and improvement can flourish to enhance patient outcomes, experience and safety.
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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 | 0.009 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".