Prevalence and correlates of suicidal ideation among the general population in China during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a major threat to the public. However, the comprehensive profile of suicidal ideation among the general population has not been systematically investigated in a large sample in the age of COVID-19. METHODS: A national online cross-sectional survey was conducted between February 28, 2020 and March 11, 2020 in a representative sample of Chinese adults aged 18 years and older. Suicidal ideation was assessed using item 9 of the Patient Health Questionnaire-9. The prevalence of suicidal ideation and its risk factors was evaluated. RESULTS: A total of 56,679 participants (27,149 males and 29,530 females) were included. The overall prevalence of suicidal ideation was 16.4%, including 10.9% seldom, 4.1% often, and 1.4% always suicidal ideation. The prevalence of suicidal ideation was higher in males (19.1%) and individuals aged 18-24 years (24.7%) than in females (14.0%) and those aged 45 years and older (11.9%). Suicidal ideation was more prevalent in individuals with suspected or confirmed infection (63.0%), frontline workers (19.2%), and people with pre-existing mental disorders (41.6%). Experience of quarantine, unemployed, and increased psychological stress during the pandemic were associated with an increased risk of suicidal ideation and its severity. However, paying more attention to and gaining a better understanding of COVID-19-related knowledge, especially information about psychological interventions, could reduce the risk. CONCLUSIONS: The estimated prevalence of suicidal ideation among the general population in China during COVID-19 was significant. The findings will be important for improving suicide prevention strategies during COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".