Safety climate in the surgical center during the Covid-19 pandemic: mixed-method study
Bibliographic record
Abstract
CONTEXT: The gradual impact of the Covid-19 pandemic had important effects on routines in surgical environments. In order to cope with the impact and re-establish anaesthesiology and surgery procedures, it was imperative to pursue in-depth studies with a view to ensuring safe surgical care, reducing hazards, as well as protecting the health, safety and wellbeing of the health personnel involved. The purpose of this study was to evaluate quantitative and qualitative approaches to domains of safety climate among multi-professional staffs of surgical centres during the Covid-19 pandemic and to identify intersections. METHODS: This mixed-method project employed a concomitant triangulation strategy on a quantitative approach in an exploratory, descriptive, cross-sectional study, as well as a qualitative approach by way of a descriptive study. Data were collected using the validated, self-applicable Safety Attitudes Questionnaire/Operating Room (SAQ/OR) questionnaire and a semi-structured interview script. The 144 participants were the surgical, anaesthesiology, nursing and support teams working in the surgical centre during the Covid-19 pandemic. RESULTS: The study found an overall safety climate score of 61.94, the highest-scoring domain being 'Communication in the surgical environment' (77.91) and the lowest, 'Perception of professional performance' (23.60). On integrating the results, a difference was found between the domains 'Communication in the surgical environment' and 'Working conditions'. However, there was intersection by the 'Perception of professional performance' domain, which permeated important categories of the qualitative analysis. CONCLUSIONS: For care practice, it is hoped to encourage improved patient safety, educational interventions to strengthen the patient safety climate and promote in-job wellbeing on the job for health personnel working in surgical centres. It is suggested that further studies explore the subject in greater depth among several surgical centres with mixed methods, so as to permit future comparisons and to monitor the evolving maturity of safety climate.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".