Analysis of the Dominant Factors Associated with the Incidence of Covid-19 in Prehospital and Intra-Hospital Nurses
Bibliographic record
Abstract
Nurses are the main lead in tackling the Covid-19 pandemic as if they are risking contracted by Covid-19. High stress during a pandemic has an impact on psychological problems, Personal Protective Equipment (PPE) usage, decision-making difficulty on triage, and fatigue in which those can decrease the immune systems. The aim of study was to identify the dominant factors related to the incidence of Covid-19 in nurses. This observational study used Cross Sectional Study design and consecutive sampling techniques at 136 respondents. Questionnaires was used as instruments tested by validity and reliability to measure Fatigue, Psychological Problems (Depression, Anxiety, Stress), Decision-Making Ability of Triage-EWS Screening Covid-19, Vaccination history, risks of exposure, PPE usage and Covid-19 Incidents. Dominant factor analysis was assessed using multivariate analysis by logistic regression test. Based on the results of the logistic regression test, it was found that the risk of exposure to Covid-19 was the dominant factor with the incidence of Covid-19 among nurses in the pre-intra-hospital settings with significance of the test results p value <0.0. Handling of Covid-19 incidents in health workers is crucial. It has been clear that the risk of exposure to people who are confirmed positive can increase the incidence of Covid-19, so it necessary to make prevention program by implementing physical distancing and increasing activities that impact on increasing of immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".