Staying ahead of the curve: Navigating changes and maintaining gains in patient safety culture - a mixed-methods study
Bibliographic record
Abstract
OBJECTIVES: This study examines how the results of the Hospital Survey on Patient Safety Culture changed between 2012 and 2019 and identifies organisational factors affecting these changes. DESIGN: The study combined the use of quantitative surveys of staff and qualitative interviews with hospital leadership. Secondary data analysis was performed for previous surveys. SETTING: This study was conducted in a tertiary care teaching multisite hospital in Riyadh, Saudi Arabia. PARTICIPANTS: One thousand hospital staff participated in the survey. Thirty-one executive board members and directors and four focus groups of frontliners were qualitatively interviewed. PRIMARY AND SECONDARY OUTCOME MEASURES: Twelve safety culture dimensions were assessed to study the patient safety culture as perceived by the healthcare professionals. An additional semi-structured interview was conducted to identify organisational factors, changes, and barriers affecting the patient safety culture. Furthermore, suggestions to improve patient safety were proposed. RESULTS: Comparing the results revealed a general positive trend in scores from 2012 to 2019. The areas of strength included teamwork within and across units, organisational learning, managerial support, overall perception of safety and feedback and communication about error. Non-punitive response to error, staffing and communication and openness consistently remain the lowest-scoring composites. Interview results revealed that organisational changes may have influenced the answers of the participants on some survey composites. CONCLUSIONS: Patient safety is a moving target with areas for improvement that are continuously identified. Effective quality improvement initiatives can lead to visible changes in the patient safety culture in a hospital, and consistent leadership commitment and support can maintain these improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".