COVID-19 disruption gets inside the family: A two-month multilevel study of family stress during the pandemic.
Bibliographic record
Abstract
Developmental research during COVID-19 suggests that pandemic-related disruptions in family relationships are associated with children's mental health. Most of this research has focused on 1 child per family, thereby obfuscating patterns that are differentially operative at the family-wide (i.e., between-family) versus child-specific (i.e., within-family) levels of analysis. Thus, the current study evaluates multilevel, longitudinal associations between COVID-19 disruption, family relationships, and caregiver/child mental health using a sibling comparison methodology. Caregivers (N = 549 families with 1098 children between 5 and 18 years old) were recruited from the Prolific research panel (73% White-European; 68% female; 76% United Kingdom, 19% U.S.A.; median 2019 income $50,000-$74,999). Caregiver reports of COVID-19 disruption, psychological distress, family functioning, parenting, and child mental health (for 2 children per family) were provided during May (time 1) and July (time 2) 2020. A Bayesian multilevel path analysis with random effects revealed: (a) families were experiencing difficulties across domains when COVID-19 disruption was high; (b) COVID-19 disruption corresponded to greater sibling differences in mental health; and (c) the sibling with poorer mental health received lower quality parenting over time, especially in families who reported higher levels of differential parenting. Findings suggest that understanding children's mental health difficulties during COVID-19 requires a family system lens due to the multiple ways these consequences permeate across the family unit. Comprehensive interventions for children's mental health during this time will likely require an examination of caregiver, sibling, and whole-family dynamics in the context of evidence-based telehealth practice. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".