Roles of Family Stress, Maltreatment, and Affect Regulation Difficulties on Adolescent Mental Health During COVID-19
Bibliographic record
Abstract
Purpose: This study examines the indirect effect of affect dysregulation and suppression on the associations between family stress from confinement, maltreatment, and adolescent mental health during COVID-19. We examined both adolescent and caregiver perspectives to yield a more well-rounded understanding of these associations. Methods: Using both adolescent (N = 809, Mage = 15.66) and caregiver (N = 578) samples, exposure to physical and psychological maltreatment, family stress from confinement, affect dysregulation, suppression, and youth externalizing and internalizing symptoms were measured in the summer of 2020, following three months of stay at home orders due to COVID-19. Results: We found that affect dysregulation partially accounted for the associations between family stress and psychological maltreatment on both internalizing and externalizing symptoms for youth and parent report. Suppression partially accounted for the associations between family stress and maltreatment on internalizing and externalizing symptoms in the youth sample, but only for internalizing symptoms in the caregiver sample. Conclusion: Understanding the family predictors of adolescents’ mental health concerns, and their underlying mechanisms, affect dysregulation and suppression, can help us target mental health interventions during and following 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".