Mental health trajectories over the COVID-19 pandemic among young adults reporting adverse childhood experiences
Bibliographic record
Abstract
Background: Higher exposure to adverse childhood experiences (ACEs) has been shown to worsen the effect of COVID-19 stress on mental health problems in the early phase of the COVID-19 pandemic among young adults. This study extends that research by examining depression, anxiety, hostility, and perceived stress trajectories across successive phases of the COVID-19 pandemic in a prospective, multi-wave panel study using data collected pre-COVID-19 pandemic onset, Early pandemic, Peak pandemic, and Post-Peak pandemic. Methods: The baseline data come from the Niagara Longitudinal Heart Study (NLHS) and the three COVID-19 waves come from a sub-study of the NLHS examining the specific impact of the pandemic. Using a Bayesian multivariate mixed-model regression framework, 171 participants who responded to at least one wave of the COVID-19 sub-study were included. Results: Participants with higher ACE scores and high COVID-19 stress had elevated trajectories of several poor mental health measures that stayed higher than other groups across all waves of data collection. Discussion: Young adults who reported higher ACEs were more susceptible to subsequent stress exposure, highlighting a specific, high-risk group who may benefit from targeted intervention programs during times of crisis such as the COVID-19 pandemic.
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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 | 0.000 | 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) | 0.001 | 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".