The Impact of COVID-19 Pandemic Stress on Mental Health among Young Adults Exposed to Adverse Childhood Experiences
Bibliographic record
Abstract
Exposure to Adverse Childhood Experiences (ACEs) has been shown to significantly impact mental health. The governmental responses to the COVID-19 pandemic, such as school and workplace closures, physical distancing measures, mandatory isolation, and quarantining, may also negatively influence mental health. However, it was unclear how ACEs and COVID-19-related stressors would impact mental health outcomes (depression, anxiety, hostility, perceived stress, and overall emotional and general health). Moreover, as young adults are likely affected to a greater extent by governmental responses than different adult cohorts, they may be in a particularly vulnerable life stage to the adverse effects of COVID-19. Therefore, this thesis aims to assess whether COVID-19-related stressors negatively impact mental health independently of exposure to ACEs among young adults and to assess whether young adults with greater exposure to ACEs were more vulnerable to the negative effects of COVID-19-related stressors on their mental health. The data used come from the Niagara Longitudinal Heart Study (NLHS), a prospective, longitudinal study that has pre-COVID-19 data (from the NLHS study) and during COVID-19 data (from a follow-up NLHS-COVID-19 sub-study survey). There were 171 participants in the study (43.7% males and 57.3% females), with 22.2% of them being exposed to 4 or more ACEs and 16.4% with no exposure to ACEs. It was found that, while exposure to COVID-19-related stressors leads to a greater reduction in mental health among young adults independent of different levels of exposure to ACEs, young adults with higher levels of ACEs were more vulnerable to the negative effects of COVID-19-related stressors on mental health. This suggests that this subgroup may benefit from intervention programs and resources directed at mental health in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".