Predictors of increased affective symptoms and suicidal ideation during the COVID-19 pandemic: results from a large-scale study of 14 271 Thai adults
Bibliographic record
Abstract
BACKGROUND: Increasing data suggest emergent affective symptoms during the COVID-19 pandemic. OBJECTIVES: To study the impact of the COVID-19 pandemic on affective symptoms and suicidal ideation in Thai adults. METHODS: The Collaborative Outcomes Study on Health and Functioning during Infection Times uses non-probability sampling (chain referring and voluntary response sampling) and stratified probability sampling to identify risk factors of mental health problems and potential treatment targets to improve mental health outcomes during pandemics. FINDINGS: Analysing 14 271 adult survey participants across all four waves of the COVID-19 pandemic in Thailand, covering all 77 provinces from 1 June 2020 to 30 April 2022, affective symptoms and suicidality increased during COVID-19 pandemic. Affective symptoms were strongly predicted by pandemic (feelings of isolation, fear of COVID-19, loss of social support, financial loss, lack of protective devices) and non-pandemic (female sex, non-binary individuals, adverse childhood experiences (ACEs), negative life events, student status, multiple mental health and medical conditions, physical pain) risk factors. ACEs, prior mental health conditions and physical pain were the top three risk factors associated with both increased affective symptoms and suicidal ideation during the COVID-19 pandemic. Partial least squares analysis showed that ACEs were the most important risk factor as they impacted most pandemic and non-pandemic risk factors. CLINICAL IMPLICATIONS: Rational policymaking during a pandemic should aim to identify the groups at highest risk (those with ACEs, psychiatric and medical disease, women, non-binary individuals) and implement both immediate and long-term strategies to mitigate the impact of ACEs, while effectively addressing associated psychiatric and medical conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".