Transforming safety culture in neonatal intensive care teams
Bibliographic record
Abstract
BACKGROUND: Healthcare organisations face widespread challenges in optimising their safety culture, especially amid conflicting stakeholder needs, staffing shortages and increasing acuity of patients. McMaster University Children's Hospital Neonatal Intensive Care Unit developed a safety culture programme that prioritises the needs of patients, hospital staff and learners altogether. METHODS: The safety culture programme and activities revolve around six primary drivers: psychological safety, provider well-being, equity, diversity and inclusion, teamwork and communication, organisational learning and leadership. We describe how these drivers influence safety culture, the ongoing activities being implemented, stakeholder feedback and contextual factors. We evaluated the maturity of our safety culture using the Manchester Patient Safety Framework (MaPSaF) questionnaire. RESULTS: MaPSaF assessments were conducted three times over 4 years. Most domains of safety culture in MaPSaF maintained their position despite COVID-19 while some indicators declined or have been maintained. CONCLUSIONS: We provide a framework for implementing a safety culture programme that addresses the needs of diverse stakeholders. Transformation of the safety culture takes time and the failure to improve the patient safety measures over the period may be attributed to rapidly increasing workload and worsening patient acuity. These challenges underscore the imperative of balancing transactional and transformational projects to preserve a safety culture.
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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 | 0.035 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".