Testing the association between the enabling and enacting factors of patient safety culture and patient safety: structural equation modelling
Bibliographic record
Abstract
BACKGROUND: Despite evidence linking a safety culture with patient safety, the processes by which aspect of safety culture influences patient safety are not yet well understood. Thus, this study aimed to test a theoretical model of the relationships between three enabling factors (supervisor/clinical leader support for patient safety, hospital management support for patient safety, and psychological safety), and four enacting factors of patient safety culture (handoffs and information exchange, teamwork, error reporting intention, and withholding voice) with nurse assessments of patient safety. METHODS: A cross-sectional, descriptive correlational study design was used. Between May and June 2020, 526 nurses who provided direct care to patients in medical surgical units in three Korean hospitals completed an online survey that included four standardized scales or subscales. Structural equation modelling was used to test the hypothesized model. RESULTS: Among the three enabling factors, psychological safety was associated with all four enacting factors, and all enacting factors were associated with overall patient safety. Hospital management support was associated with all enacting factors except teamwork, but supervisor/clinical leader support was associated with only handoffs and information exchange, and withholding voice. Thus, teamwork was influenced only by psychological safety. Findings demonstrate overall support for the theoretical model of safety culture wherein enabling factors influence enacting factors which, in turn, lead to patient safety outcomes, but emphasize the critical nature of psychological safety among nursing staff. CONCLUSION: This study provides further insight into the importance of support from hospital management and unit supervisors/clinical leaders for patient safety to motivate and enable hospital nurses to enact behaviours necessary for patient safety. However, such support must also take the form of enhancing psychological safety for nursing staff.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".