Mental Health of Frontline Nurses in India During COVID-19: A Multisite Study
Bibliographic record
Abstract
Background The COVID-19 pandemic has been difficult for all healthcare providers. Nurses in Indian hospitals are at risk for mental health consequences of COVID-19-related stress. The study aimed to evaluate the mental health responses of Indian nurses working during the COVID-19 pandemic. Method The study was carried out during the COVID-19 pandemic from November 2020 to February 2021. Frontline nurses (n=387) working in both government and private sectors were recruited from four hospital centers across Mangalore, India. Nurses were selected based on specific inclusion criteria, including active duty within wards and intensive care units designated for COVID-19 care or suspected cases of SARS-CoV-2 infection. Recruitment and data collection were facilitated by medical residents using a mix of physical and electronic survey methods. Results Nurses within the private sector with low personal protective equipment (PPE) security experienced heightened anxiety. Somatic symptoms were incrementally related to mental health depending on the workplace setting; private sector staff reported greater depression symptoms compared to those in government-run hospitals. Self-efficacy buffered against depression outcomes only in nurses within the private sector working within non-COVID units. Conclusions This study's findings showed differential responses to the stress of COVID-19 based on the setting. Future studies should further explore the factors associated with such differences. Somatic symptoms can be indicators of mental health adversity. Early detection and supportive interventions need to be taken into account.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".