Nurses' Opinions Regarding Comments from a Patient Safety Culture Evaluation
Bibliographic record
Abstract
Background: Ensuring a strong patient safety culture is essential for the delivery of high-quality healthcare. In low- and middle-income countries (LMICs), inadequate and unsafe medical care is responsible for nearly 60% of fatalities, many of which are preventable. A positive patient safety culture fosters trust, openness, and performance improvement. Understanding healthcare workers' perceptions of safety practices is a crucial step in enhancing patient safety culture. Methods: This study was conducted using a self-administered online survey among 1000 healthcare professionals in a major general hospital. The Hospital Survey on Patient Safety Culture (HSOPSC) was utilized, assessing 42 items across 12 dimensions of patient safety culture. 700 responses were analyzed. Descriptive statistics, positive response percentages, and multiple linear regression were used for data analysis. Results: Overall, 76.9% of respondents rated the patient safety grade as excellent or very good. The patient safety culture composite score was 74.2%, with strengths in areas like "Teamwork within units" (91.3%) and "Organizational learning" (88.4%). However, areas needing improvement included "Staffing" (49.4%) and "Non-punitive response to errors" (53.1%). A majority of respondents (67.1%) had not reported any safety events in the past year. Female healthcare workers and nurses reported lower perceptions of patient safety compared to male and physician respondents. Additionally, work area/unit influenced perceptions, with emergency and surgery departments having better safety perceptions. Conclusion: The study highlights a generally positive perception of patient safety culture in the hospital, though areas such as staffing and non-punitive responses to errors require improvement. Gender, position, and work area/unit were significant predictors of safety perceptions. These findings emphasize the need for targeted interventions to enhance patient safety culture, with a focus on improving staffing and fostering a non-punitive environment for reporting 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 | 0.015 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".