Survey of Nursery Errors in Healthcare Centers, Isfahan, Iran
Bibliographic record
Abstract
BACKGROUND & AIM: Nurse's mistakes usually have a strong effect on the patients trust and satisfaction in the health services systems, and it can also lead to stress and moral contradicts among nurses. This study has aimed to survey the rate of nurses' mistakes, according to documents in the Isfahan Province during 2007-2012. METHODS: The study was a descriptive cross-sectional study. The sample population consisted of all complaints concerning nursing services provided in hospitals, private clinics and other health service centers between 2007 and 2012, submitted to the Forensic Medicine Commission Office, in Isfahan. The data were collected by a cheklist and analyzed using SPSS version 16.0 software. RESULTS: Out of 708 complaints, 70 (9.8%) cases were related to nurses. Twenty-four cases led to awards. The age range of nurses was 35-40 (25.7%). Out of 70 nurses with a record, 75% (53 people) were female and the rest were male. Sixty four nurses (91.4%) were working in hospitals. Negligence was the first basis of the court rulings (16 cases out of 24). Nurses' recklessness in providing services was due to their convictions among 66.7% of the cases. CONCLUSION: Although efforts to reduce and control nurses' faults and mistakes depends on using a system for studying and removing the factors which lead to faults, human error is inevitable in every occupation and a 100% accurate operation is unreachable.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".