Parental Mental Health and Hostility Are Associated With Longitudinal Increases in Child Internalizing and Externalizing Problems During COVID-19
Bibliographic record
Abstract
Children are at high risk for negative COVID-19 related outcomes. The present longitudinal study assessed (1) changes in child internalizing and externalizing problems from before to during the pandemic and (2) whether parent mental health (depression, anxiety, stress) or parenting behavior during COVID-19 were associated with changes in child mental health problems. Sixty eight mother-child dyads participated in this study. Children were approximately five years-old at the time of enrollment and were between the ages of 7–9 years old at the time of the follow-up survey. Parenting behavior, parental depression, anxiety, perceived stress and child internalizing and externalizing problems were measured using validated questionnaires. Children experienced greater internalizing (t = 6.46, p < 0.001) and externalizing (t = 6.13, p < 0.001) problems during the pandemic compared to before the pandemic. After taking into account child gender and COVID-related stressors, parental hostility was uniquely associated with greater changes in externalizing problems (β = 0.355, SE = 0.178, p < 0.05), while maternal anxiety was associated with greater increases in internalizing problems (β = 0.513, SE = 0.208, p < 0.05). Findings highlight the need for mental health supports for families to limit the impact of the COVID-19 pandemic on child and parent mental health.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".