Risk Factors for COVID-19: A Quantitative Study Conducted at Padang City Center Hospital
Bibliographic record
Abstract
Objective: This study sought to estimate the prevalence of COVID-19 infection among hospital staff according to various factors. Moreover, it sought to identify any factors that predicted a higher probability of infection in this population. Methods: This descriptive research was conducted among medical and non-medical personnel at Padang City Center Hospital, Indonesia (n=129). A chi-square test analysis was used to determine the degree of interrelationship between the studied variables, while an odds ratio (OR) test was performed to identify more potential categories. Results: Some 31.8% of respondents tested positive for COVID-19, although this finding was insignificant (p>0.05). In terms of the OR, the following probabilities were calculated: age (OR=1.0 [0.36–2.88]); medical history (OR=1.3 [0.23–2.0]); higher education (OR=1.9 [0.2–17.6]); wearing a good mask (OR=0.7 [0.07–7.02]); good hand washing (OR=1.8 [0.46–7.07]); good physical distancing (OR=1.8 [0.46–7.07]); good personal protective equipment (OR=0.7 [0.07–7.02]); normal depression, anxiety, and stress (OR<1.0); and comorbidity (OR=1,2 [0.46-3.06]). Conclusion: No significant relationship was found between the studied factors and COVID-19 infection. However, there were more potential trends, especially for highly educated medical teams, not wearing a mask, smoking, engaging in strenuous activity, poor psychology, and comorbidity. These findings should prompt policymakers tasked with developing resources and interventions to pay more attention to the needs of medical and non-medical staff during the COVID-19 pandemic, especially the availability of masks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".