Positive childhood experiences serve as protective factors for mental health in pandemic-era youth with adverse childhood experiences
Bibliographic record
Abstract
BACKGROUND: While adverse childhood experiences (ACEs) predict poorer mental health across the life course, positive childhood experiences (PCEs) predict better mental health. It is unclear whether PCEs protect against poor mental health outcomes and promote mental well-being in pandemic-era adolescents with ACEs. METHODS: We examined the individual and joint contributions of ACEs and PCEs to mental health and well-being (MHW) in eleventh-grade British Columbian adolescents (N = 8864) during the fifth wave of COVID-19. We used a novel measure of ACEs that included community- and societal-level ACEs in addition to ACEs experienced at home to investigate the role of social and structural determinants of mental health in supporting the MHW of pandemic-era adolescents. A series of two-way ANCOVAs were conducted comparing MHW outcomes between adolescents with and without ACEs. Interaction effects were examined to investigate whether PCEs moderated the association between ACEs and MHW. RESULTS: Adolescents with no ACEs had significantly better MHW than those with one or more ACE. Having six or more PCEs was associated with better MHW in adolescents with and without ACEs. PCEs significantly moderated the association between ACEs and depression. Effect sizes were larger for PCEs than ACEs in relation to depression, mental well-being, and life satisfaction. CONCLUSIONS: PCEs may protect against depression among adolescents with ACEs and promote MHW among all pandemic-era adolescents. These findings emphasize the importance of addressing social determinants of mental health to mitigate the impact of ACEs and promote PCEs as part of a public health approach to MHW.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".