Effect of the second wave of COVID on mental health of the general population of India
Bibliographic record
Abstract
BACKGROUND: The first wave of COVID-19 in India was followed by a far more catastrophic second wave with an unprecedented surge in cases and increased deaths. In addition, studies conducted during the first wave reported a higher-than-average prevalence of psychiatric morbidity.METHODS: The study aimed to measure psychiatric morbidity and COVID-related anxiety among the general population during the more impactful second wave in India. The study also found a correlation between the two variables. A cross-sectional survey was conducted using online questionnaires. GHQ-28 and CAS were used. Open-ended questions regarding people’s thoughts, feelings, and reactions to the second wave were also added.RESULTS: The prevalence of psychiatric morbidity and COVID-related anxiety was 49.07% and 41.26%, respectively. A moderate positive correlation (0.661, P=0.000) was found between GHQ-28 and CAS scores. Women, younger, unmarried, unemployed, less educated participants had higher mean scores on both scales. The major themes that emerged from the content analysis of the qualitative results are- anxiety/fear, depression, hope/optimism, self-rediscovery and, a sense of social responsibility and altruism. Most people rendered the second wave more distressing, attributing it to unavailability of medical resources, the government’s incompetence, and people’s irresponsibility.CONCLUSIONS: The further deterioration of the mental health of Indians during the second wave highlights the need for immediate interventions. Further research is necessary to identify vulnerable populations needing greater assistance. These interventions should inform policy related to metal health issues in the future.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".