Changing Trends in Suicidal Ideation and Its Influencing Factors During the Transition From Quarantine to Post-Quarantine Among Chinese University Students During the COVID-19 Surge: Six-Wave Panel Study
Bibliographic record
Abstract
Background: To mitigate the rapid spread of COVID-19, numerous countries have adopted lockdowns and quarantine measures. Despite their public health benefits, the effects of these measures on suicidal ideation have not been well documented. Objective: This study aims to examine the relationship among COVID-19 infection, perceived beliefs, uncertainty stress, and suicidal ideation during the transition from quarantine to post-quarantine periods amid China's COVID-19 surge. Methods: A prospective longitudinal observational design was used. Changing trends across the 6 time points were assessed using the Mann-Kendall test and the Cochran-Armitage test. A generalized estimating equation was used to analyze the associations between independent variables and suicidal ideation. Results: A total of 221 (96.5%) participants completed all 6 observation waves. The prevalence of suicidal ideation during the quarantine period was 16.7% (n=37), 14.5% (n=32), and 14.5% (n=32), while during the post-quarantine period, it was 13.8% (n=30), 10.9% (n=24), and 10.0% (n=22), respectively. A significant downward trend in suicidal ideation was observed. In contrast, perceived risk, perceived severity, and the number of new infections exhibited significant upward trends (z scores of 9.56, 7.13, and 3.69, respectively; P<.001, P<.001 and P=.002, respectively). However, uncertainty stress remained stable over time (z=0.71; P=.48). The generalized estimating equation indicated that perceived risk (β=0.5482; P<.001), perceived severity (β=0.0817; P=.007), and uncertainty stress (β=0.1776; P<.001) were positively associated with suicidal ideation. The number of new infections (β=0.0041; P=.49) was not significantly associated with suicidal ideation. Conclusions: This study found that suicidal ideation gradually declined following the lifting of quarantine measures. Perceived risk, perceived severity, and uncertainty stress, rather than numbers of infected cases, were significantly associated with suicidal ideation. These findings highlight the importance of addressing individuals' perceptions with real-world context when developing effective strategies to manage COVID-19 and future infectious disease outbreaks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".