Exploring the Changes of Suicide Probability During COVID-19 Among Chinese Weibo Users
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it