Exploring the Changes of Suicide Probability During COVID-19 Among Chinese Weibo Users
Bibliographic record
Abstract
Abstract. Background: Coronavirus disease 2019 (COVID-19) threatens people's physical and mental health, globally, and it may even trigger suicide ideation and suicidal behavior. Aims: We aimed to examine the impact of COVID-19 on suicide risk by sampling Chinese Weibo users and analyzing their social media messages. Method: We predicted the probability of suicide (including hopelessness, suicidal ideation, negative self-evaluation, and hostility) of Weibo users in order to assess the changes in suicide probability at different times. Repeated-measures ANOVA was performed to examine the differences in suicide probability in different regions during different periods. Results: There was no significant difference in suicide probability between profoundly infected areas (PIAs) and less infected areas (LIAs) before the outbreak of COVID-19. LIAs had an increase in hopelessness during the COVID-19 growth period, while hopelessness and hostility in PIA increased during the COVID-19 decline period, indicating potential suicide probability. Limitations: Results should be interpreted with caution, and cross-cultural research may be considered in the future. Conclusion: COVID-19 has a dynamic impact on suicide probability. Using data from online social networks may help to understand the impact pattern of COVID-19 on people's suicide probability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".