Mental Health Risks after Repeated Exposure to Multiple Stressful Events during Ongoing Social Unrest and Pandemic in Hong Kong: The Role of Rumination: Risques pour la santé mentale après une exposition répétée à de multiples événements stressants d’agitation sociale durable et de pandémie à Hong Kong: le rôle de la rumination
Bibliographic record
Abstract
Objectives: The co-occurrence of different classes of population-level stressors, such as social unrest and public health crises, is common in contemporary societies. Yet, few studies explored their combined mental health impact. The aim of this study was to examine the impact of repeated exposure to social unrest-related traumatic events (TEs), coronavirus disease 2019 (COVID-19) pandemic-related events (PEs), and stressful life events (SLEs) on post-traumatic stress disorder (PTSD) and depressive symptoms, and the potential mediating role of event-based rumination (rumination of TEs-related anger, injustice, guilt, and insecurity) between TEs and PTSD symptoms. Methods: Community members in Hong Kong who had utilized a screening tool for PTSD and depressive symptoms were invited to complete a survey on exposure to stressful events and event-based rumination. Results: A total of 10,110 individuals completed the survey. Hierarchical regression analysis showed that rumination, TEs, and SLEs were among the significant predictors for PTSD symptoms (all P < 0.001), accounting for 32% of the variance. For depression, rumination, SLEs, and PEs were among the significant predictors (all P < 0.001), explaining 24.9% of the variance. Two-way analysis of variance of different recent and prior TEs showed significant dose-effect relationships. The effect of recent TEs on PTSD symptoms was potentiated by prior TEs ( P = 0.005). COVID-19 PEs and prior TEs additively contributed to PTSD symptoms, with no significant interaction ( P = 0.94). Meanwhile, recent TEs were also potentiated by SLEs ( P = 0.002). The effects of TEs on PTSD symptoms were mediated by rumination (β = 0.38, standard error = 0.01, 95% confidence interval: 0.36 to 0.41), with 40.4% of the total effect explained. All 4 rumination subtypes were significant mediators. Conclusions: Prior and ongoing TEs, PEs, and SLEs cumulatively exacerbated PTSD and depressive symptoms. The role of event-based rumination and their interventions should be prioritized for future research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".