Evaluation of Patient Safety Indicators in Semnan City Hospitals by Using the Patient Safety Friendly Hospital Initiative (PSFHI)
Bibliographic record
Abstract
BACKGROUND: Nowadays, patient safety issue is among one of the main concerns of the hospital policy worldwide. This study aimed to evaluate the patient safety status in hospitals affiliated to Semnan city, using the WHO model for Patient Safety Friendly Hospital Initiatives (PSFHI) in summer 2014. METHODS: That was a cross sectional descriptive study that addressed patient safety , which explained the current status of safety in the Semnan hospitals using by instrument of Patient safety friendly initiative standards (PSFHI). Data was collected from 5 hospitals in Semnan city during four weeks in May 2014. RESULTS: The finding of 5 areas examined showed that some components in critical standards had disadvantages. Critical standards of hospitals including areas of leadership and administration, patient and public involvement and safe evidence-based clinical practice, safe environment with and lifetime education in a safe and secure environment were analyzed. The domain of patient and public involvement obtained the lowest mean score and the domain of safe environment obtained the highest mean score in the surveyed hospitals. CONCLUSION: All the surveyed hospitals had a poor condition regarding standards based on patient safety. Further, the identified weak points are almost the same in the hospitals. Therefore, In order to achieve a good level of all aspects of the protocol, the goals should be considered in the level of strategic planning at hospitals. An effective execution of patient safety creatively may depend on the legal infrastructure and enforcement of standards by hospital management, organizational liability to expectation of patients, safety culture in 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".