Suicide related to the COVID-19 pandemic in India: A systematic review
Bibliographic record
Abstract
Abstract Background The suicide rate has increased during the pandemic in India. Moreover, several studies, especially press-media reporting suicide studies have been conducted but no systematic review has been attempted in this context. Therefore, the present study systematically investigated the risk factors associated with suicidal behaviors, and the method of suicide during the COVID-19 pandemic in India. Methods Following the PRISMA guidelines, a systematic search was performed to include papers published up until September 30, 2022. From an initial 144 papers, 18 studies which met the inclusion criteria were included in the present review. The Pierson’s method was used for quality assessment of the included studies in the present review. Results The risk factors associated with suicide comprised: (i) socio-demographic factors (e.g., being aged between 31 and 50 years, male, married, unemployed), (ii) behavior and health-related factors (e.g., unavailability of alcohol and alcohol withdrawal symptoms, poor state of physical health and health issues, family disputes, relationship complexities, and sexual harassment), (iii) COVID-19-related factors (e.g., fear of COVID-19, COVID-19 test results, quarantine or isolation, financial hardship due to the pandemic, having influenza-like symptoms, experiencing stigmatization and ostracism despite testing negative, separation from family due to transport restrictions, misinterpreting other illness symptoms as COVID-19, saving the village from infection, watching COVID-19 videos on social media, online schooling, perceived stigma toward COVID-19, and being suspected of having COVID-19), and (iv) psychopathological stressors (depression, loneliness, stress, TikTok addiction, and poor mental health, suicidal tendencies, helplessness, and worrying). Hanging was the most common method of suicide. In addition, jumping from high buildings, poisoning, drowning, burning, cutting or slitting throat or wrists, self-immolation, medication overdose, electrocution, pesticide, and gun-shot were also used to carry out the suicide. Conclusions Findings from this research suggest multiple reasons for suicide during the COVID-19 pandemic and knowledge of such factors could aid in developing suicide prevention strategies focusing the most vulnerable cohorts inside and outside India.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".