Safety culture in the context of operating room: Nurses' perception regarding notification of errors/adverse events
Bibliographic record
Abstract
The notification of errors/adverse events is one of the central aspects for the quality of care and patient safety. The purpose of this pilot study is to analyse the safety culture of the operating room in relation to the errors/adverse events and their notification, in the nurses’ perception. It is a quantitative, descriptive-exploratory pilot study. A survey “Nurses’ Perception regarding Notification of Errors/Adverse Events” was applied, consisting of 8 closed questions to an intentional non-probabilistic sample consisting of 43 nurses working in the operating room of a private hospital in Lisbon. The results showed that only 51.2% of the adverse events that caused damage to patients were always notified by the nurses. Of the various adverse events occurred, 60.5% were not reported, justified by “lack of time”. There was also a negative correlation between professional experience and the frequency of error notification (p < .05). The factors referred as those that contributed most to the occurrence of errors were, pressure to work quickly (100.0%), lack of human resources (86.0%), demotivation (86.0%), professional inexperience and hourly overload (83.7%), lack of knowledge (74.4%) and communication failures (65.1%). The perception of Patient Safety was assessed by the majority of participants as “acceptable”. In conclusion, it was evident the reduced notification of adverse events in the operation room so it becomes crucial to focus on the continuous training of health professionals, as well as work on the error, to increase a safety culture with quality.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".