Non-technical skills in surgery during the COVID-19 pandemic: An observational study
Bibliographic record
Abstract
BACKGROUND: Non-technical skills are critical to surgical safety. We examined the impact of the COVID-19 pandemic on non-technical skills of operating room (OR) teams in Singapore. MATERIALS AND METHODS: Observers rated live operations using the Oxford NOTECHS system. Pre- and post-COVID observations were captured from November 2019 to January 2020 and from January 2021 to February 2021, respectively. Scores were compared using Schuirmann's Two One-Sided Test procedure. Multivariable linear regression was used to adjust for case mix. A 10% margin of equivalence was set a priori. RESULTS: Observers rated 159 cases: 75 pre-COVID and 84 post-COVID. There were significant differences between groups in surgical department and surgeon-reported case complexity (both P < 0.001). Total NOTECHS scores increased post-COVID on raw analysis (36.1 vs 38.0, P < 0.001) but remained within the margin of equivalence (90% CI 1.3 to 2.6, P < 0.001). Multivariable analysis demonstrated a similar increase within the margin of equivalence (2.0, 90% CI 1.3 to 2.7). Teamwork and cooperation scores increased by 1.0 post-COVID (90% CI 0.8 to 1.3); all other subcomponent scores were equivalent. CONCLUSION: Non-technical skills before and after the peak of the COVID-19 pandemic were equivalent but not equal. A small but statistically significant improvement post-COVID was driven by an increase in teamwork and cooperation skills. These findings may reflect an improvement in team cohesion, which has been observed in teams under duress in other settings such as the military. Future work should explore the effect of the pandemic on OR culture, team cohesion, and resilience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".