The Impact of the COVID-19 Pandemic on the Mental Health of Young Adults with ACEs
Bibliographic record
Abstract
The impact of Adverse Childhood Experiences (ACEs) on mental health is well-established. Additionally, the measures taken by the government in response to the COVID-19 pandemic, such as school and workplace closures, physical distancing, isolation, and quarantining, also seem to have a negative influence on mental health. However, the specific combined effects of ACEs and COVID-19-related stressors on mental health outcomes like depression, anxiety, hostility, and perceived stress were not clearly understood. Given that young adults may be more significantly affected by the government's responses to the pandemic than other adult age groups, they could be particularly vulnerable to the adverse effects of COVID-19. Therefore, the two aims of this thesis were to determine if young adults with high COVID-19 stress had higher levels of mental health problems and whether those with higher exposure to ACEs were more susceptible to the negative consequences of COVID-19-related stressors on their mental health. To carry out this research, survey data were collected from the Niagara Longitudinal Heart Study (NLHS), which is a longitudinal study that had pre-COVID-19 data, as well as data from three phases of the COVID-19 pandemic collected using a series of follow-up NLHS-COVID-19 sub-study surveys. The study included 138 (171 included in longitudinal, mixed-model regression analysis) as the final sample of participants, with 41.5% males and 58.5% females. Among them, 21% had been exposed to four or more ACEs, while 17.4% had no exposure to ACEs. The findings revealed that the higher exposure to COVID-19-related stressors resulted in increased levels of hostility and anxiety among Canadian young adults. Notably, young adults with higher ACEs were particularly susceptible to the detrimental effects of these stressors on their mental health across different phases of the pandemic. This highlights the need for targeted intervention programs and mental health resources to support this vulnerable subgroup during times of crisis, like 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 | 0.001 |
| 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".