Adverse Childhood Experiences and Vulnerability to Mood and Anxiety Disorders During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic had a global impact on mental health. Identification of individuals at higher or lower risk of mental health problems may assist with targeting prevention, support and treatment efforts during future pandemics. Using a Canadian national mental health survey that collected data during the pandemic period (March 2022–December 2022), this study examined the vulnerability of participants reporting abuse during their childhood by examining the annual prevalence of mood, anxiety and substance use disorders. Psychiatric disorders were identified using a version of the Composite International Diagnostic Interview (CIDI). Because childhood adversities are well-known risk factors for mental disorders, the analysis focused on interactions between childhood adversities and pandemic-related stressors by estimating the relative excess risk due to interaction (RERI). RERIs provide evidence of synergy based on the occurrence of greater than additive interactions. Physical and sexual abuse interacted synergistically with pandemic-related stressors in predicting mood and anxiety disorders. No synergies were found for substance use disorders. Childhood adversities increase vulnerability to later stressors and may be useful for the identification of individuals more likely to have mental health needs during this type of public health emergency.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".