Prevalence and measurement of anxiety and depression in nurses during COVID pandemic in Nepal
Bibliographic record
Abstract
Background: Anxiety and depression are under reported, underdiagnosed mental illness in health worker in Nepal especially during COVID pandemic. The study was carried out as an observational study on nurses in Nepal. In this study we attempted to assess the incidence and impact of depression and anxiety in nurses who are working upfront in different hospitals during this crisis. Objective: The purpose of the study is to assess the prevalence of anxiety and depression among nurses in Nepal during COVID pandemic who are working in various hospitals. Method: A cross-sectional non-probability purposive sampling with observational analysis was carried out and the sample was collected from nurses working in different hospitals. Prevalence of anxiety and depression was assessed using a structured and validated questionnaire. Anxiety was assessed with the Hamilton Anxiety Scale (HAM-A), General Anxiety Disorder Questionnaires (GAD) with a cut-off score for various levels of anxiety while Hamilton Depression Rating Scale (HAM-D) was used to assess depression. Result: The analysis of these different scales revealed that disabling anxiety prevailed at highest (43.6%) in nursing staff according to HAM-A scale. Moderate anxiety also seemed to be higher (> 20%) in GAD questionnaire. Conclusion: This is the first study carried out in Nepal that investigates the mental health of nurses who are working in the frontline in this COVID pandemic situation. The study revealed that our nurses who have given their life in the line are suffering from serious mental health problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".