Tingkat Kecemasan Perawat dalam Menangani Pasien Covid-19 di Wilayah Kabupaten Blora
Bibliographic record
Abstract
Nowadays, the world is still at the time of the COVID-19 pandemic, where cases are still increasing every day. Nurses as the front line in treating COVID-19 patients have a major role in providing direct services to patients. Therefore, nurses are at high risk of dealing with psychological conditions such as anxiety. Nurses who are directly involved in handling and treating COVID-19 patients are at risk of experiencing psychological issues. Method: This research is descriptive research using a cross-sectional with a survey via google form. The population in this research were nurses at Three Blora District Hospitals, they are RSUD Dr. R. Soetijono Blora, RSU Permata Blora, and RSUD Dr. R. Soeprapto Cepu. Researchers will use a sample of 20 nurses in each hospital. Results: The results of the analysis of the data collected from the 40 COVID-19 nurses revealed that 52,5% of the respondents experienced mild anxiety; 27,5% had moderate anxiety; 10% had severe anxiety; while 10% did not experience any anxiety. Covid-19 nurses who are women, 35-45 years old, Diploma Three of Nursing tend to have higher levels of anxiety. Conclusion: Marital status, hospital support and depression levels are the factors that affect the level of anxiety among COVID-19 nurses in hospitals. Hospital support is a significant factor affecting anxiety levels.Keywords: Covid-19, Nurses, Anxiety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.007 |
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".