Work climate in emergency health services during COVID-19 pandemic—An international multicenter study
Bibliographic record
Abstract
Introduction: A good working climate increases the chances of adequate care. The employees of Emergency in Hospitals are particularly exposed to work-related stress. Support from management is very important in order to avoid stressful situations and conflicts that are not conducive to good work organization. The aim of the study was to assess the work climate of Emergency Health Services during COVID-19 Pandemic using the Abridged Version of the Work Climate Scale in Emergency Health Services. Design: A prospective descriptive international study was conducted. Methods: The 24-item Abridged Version of the Work Climate Scale in Emergency Health Services was used for the study. The questionnaire was posted on the internet portal of scientific societies. In the study participated 217 women (74.5%) and 74 men (25.4%). The age of the respondents ranged from 23 to 60 years (SD = 8.62). Among the re-spondents, the largest group were Emergency technicians (85.57%), followed by nurses (9.62%), doctors (2.75%) and Service assistants (2.06%). The study was conducted in 14 countries. Results: The study of the climate at work shows that countries have different priorities at work, but not all of them. By answering the research questions one by one, we can say that the average climate score at work was 33.41 min 27.0 and max 36.0 (SD = 1.52). Conclusion: The working climate depends on many factors such as interpersonal relationships, remuneration or the will to achieve the same selector. In the absence of any of the elements, a proper working climate is not possible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".