Patient safety culture through the perspectives of healthcare workers: a longitudinal study in a private healthcare network in Brazil
Bibliographic record
Abstract
BACKGROUND: Enhancing security and dependability of health systems necessitates resource allocation, a well-defined infrastructure and a steadfast commitment to ensuring its safety and stability over time. OBJECTIVE: The aim of this study was to evaluate the temporal trend of patient safety culture according to the perception of professionals working in a private healthcare network in Brazil over a 7-year period (2015-2022). METHODS: The Hospital Survey on Patient Safety Culture questionnaire was distributed to 34 hospitals between 2015 and 2022 with 160 607 responders. A linear mixed-effects regression model was applied to fit the trend for the dimension score over time. RESULTS: Out of the 12 measured dimensions in the HSOPSC Survey, 8 showed significant improvement over a 7-year period (p<0.05). The dimensions of communication openness (p=0.22), non-punitive response to errors (p=0.08), staffing (p=0.06) and the frequency of reported events (p=0.22) have not demonstrated improvement over time. Management support for patient safety and organisational learning received positive responses from at least 75% of those surveyed in 2022, earning the distinction as 'strong areas of patient safety'. Comparing 2015 and 2022, the proportion of participants who rated their unit/work area on patient safety as 'fair' or 'good' decreased, while the proportion of participants who considered it 'very good' increased (p<0.001). CONCLUSIONS: Findings indicate an improvement in patient safety culture from 2015 to 2022. Key challenges identified in enhancing safety culture included communication openness, staffing, frequency of reported events, and nonpunitive response to errors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".