Translation and Psychometric Properties of the Persian Version of Oxford Non-technical Skills 2 System: Assessment of Surgical Teams' Non-technical Skills in Orthopedic Surgery Wards.
Bibliographic record
Abstract
BACKGROUND: Non-technical skills are interpersonal and cognitive skills involved in safe performance and preventing adverse events during surgery. it is necessary to dominate the non-technical skills to ensure patient safety. This study has aimed to assess the validity and reliability of Oxford Non-technical skills 2 system (Oxford NOTECHS 2) in Iran and to evaluate surgical teams' non-technical skills in orthopedic surgery wards. METHODS: This cross-sectional study was conducted in Tehran, Iran during 2015. The level of evidence is III based on Canadian Task Force on the Periodic Health Examination. We followed the Beaton's guideline for Persian translation and cross-cultural adaptation of the checklist. In this study, 60 orthopedic surgical team members working in two selected public hospitals were selected by cluster random sampling method.Oxford NOTECHS 2 system which is consisted of four subscales including leadership and management, teamwork and collaboration, decision-makingand problem-solving, and situational awareness was used to collect the data. RESULTS: The overall mean score of non-technical skills was 69.52±6.64. The mean score for surgery, anesthesia, and nursing sub-teams were 24.98±3.71, 21.12±4.29, and 23.42±3.60, respectively. The teams' scores in total, leadership and management, teamwork and collaboration, problem solving and decision making, and situational awareness at the standard level were 74.70%, 76.95%, 73.75%, 66.87%, and 74.70% of maximum score, respectively. CONCLUSION: The validity and reliability of the Persian version of Oxford NOTECHS 2 scale in Iran was confirmed. The results of this study showed that surgical teams' non-technical skills were at a moderate level in orthopedic surgery wards. The minimum score of the surgical teams' non-technical skills belonged to anesthesia and maximum to surgery sub-team. Using the training programs and setup workshop is recommended to improve the surgical teams' non-technical skills, especially surgery-nursing sub-team.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".