Safety culture in healthcare: a review of concepts, dimensions, measures and progress
Bibliographic record
Abstract
BACKGROUND: A growing body of peer-reviewed studies demonstrate the importance of safety culture in healthcare safety improvement, but little attention has focused on developing a common set of definitions, dimensions and measures. OBJECTIVES: Specific objectives of this literature review include: summarising definitions of safety culture and safety climate, identifying theories, dimensions and measures of safety culture in healthcare, and reviewing progress in improving safety culture. METHODS: Peer-reviewed, English-language articles published from 1980 to 2009 pertaining to safety culture in healthcare were reviewed. One hundred and thirty-nine studies were included in this review. RESULTS: Results suggest that there is disagreement among researchers as to how safety culture should be defined, as well as whether or not safety culture is intrinsically diverse from the concept of safety climate. This variance extends into the dimensions and measurement of safety culture, and interventions to influence culture change. DISCUSSION: Most studies utilise quantitative surveys to measure safety culture, and propose improvements in safety by implementing multifaceted interventions targeting several dimensions. Conversely, very few studies made their theoretical underpinnings explicit. Moving forward, a common set of definitions and dimensions will enable researchers to better share information and strategies to improve safety culture in healthcare, building momentum in this rapidly expanding field. Advancing the measurement of safety culture to include both quantitative and qualitative methods should be further explored. Using the expertise of traditional culture experts, anthropologists, more in-depth observational and longitudinal research is needed to move research in this area forward.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".