A Cross-Sectional Study on Patient Safety Culture in a Tertiary Care Hospital in India
Bibliographic record
Abstract
Aim: The study was to assess the patient safety culture in a tertiary care hospital in India using the HSOPSC survey. It also sought to compare the findings with global data to identify strengths and areas for improvement in patient safety practices. Methods: This cross-sectional study used the Hospital Survey on Patient Safety Culture (HSOPSC) version 2.0 to assess patient safety culture at a tertiary care hospital in India. The survey was administered to healthcare professionals across various specialties, with responses analyzed using SPSS software. Comparative analysis was conducted with global data from the AHRQ database to evaluate differences in patient safety perceptions. Findings: Findings revealed that while the hospital performed well in areas like organizational learning and communication about errors, it scored lower in domains such as staffing, error reporting, and teamwork compared to global data. A significant portion of staff reported challenges with staffing levels and work pace. Error reporting was less frequent, with many staff members indicating underreporting of incidents. Teamwork and communication within multidisciplinary teams also showed room for improvement, especially in overcoming hierarchical barriers. These results emphasize the need for better staffing practices, a supportive reporting environment, and enhanced interprofessional collaboration. Conclusion: This study highlights the strengths and weaknesses in patient safety culture at a tertiary care hospital in India, with notable gaps in staffing, error reporting, and teamwork. Addressing these challenges through improved staffing levels, non-punitive reporting systems, and team-building initiatives could enhance patient safety. The findings suggest that fostering a supportive and open safety culture is essential for reducing medical errors. The study provides valuable insights for healthcare policymakers to implement targeted interventions for safer patient care in Indian hospitals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".