Quarantine and isolation nurses conditions during COVID-19 in Jeddah City
Bibliographic record
Abstract
Background: The novel coronavirus is a pandemic respiratory disease that spreads from person to person. As the outbreak continues to develop, several countries have taken measures to prevent the transmission of the disease. Some of these measures are quarantine and isolation. Nurses are on the front line of the health care system during the COVID-19 outbreak, and they may become overwhelmed by the numerous pressures, such as the risk of infection, inadequate supplies, workload, and frustration. The aim of this study is to identify nurses’ conditions in quarantine and isolation during the COVID-19 outbreak in Jeddah city.Methods: A cross-sectional descriptive design, a questionnaire was used for data collection.Results: About 60% of the participants were willing to continue working in quarantine and isolation. The main reasons they were willing to continue were a safe working environment (37.3%) and effective teamwork (31.8%), while the main reasons that made the participants unwilling to continue were the lack of rewards and incentives (28.3%), and an unsafe work environment (40.0%). Regarding the nurses’ assessment of quarantine and isolation conditions during the COVID-19, most participants confirmed that they often choose a positive element. This study found a significant difference according to the current place of work, which was positive on the side of the nurses who are working in quarantine.Conclusions: This study’s findings provide important recommendations related to the improvement of quarantine and isolation conditions, staff nurses’ conditions, and disease-outbreak-related readiness. It is hoped that these recommendations will contribute to enhance the current and future conditions of nurses in isolation and quarantine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".