Empirical support for a model of risk and resilience in children and families during COVID-19: A systematic review & narrative synthesis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 Family Disruption Model (FDM) describes the cascading effects of pandemic-related social disruptions on child and family psychosocial functioning. The current systematic review assesses the empirical support for the model. METHODS: Study eligibility: 1) children between 2-18 years (and/or their caregivers); 2) a quantitative longitudinal design; 3) published findings during the first 2.5 years of COVID-19; 4) an assessment of caregiver and/or family functioning; 5) an assessment of child internalizing, externalizing, or positive adjustment; and 6) an examination of a COVID-19 FDM pathway. Following a search of PsycINFO and MEDLINE in August 2022, screening, full-text assessments, and data extraction were completed by two reviewers. Study quality was examined using an adapted NIH risk-of- bias tool. RESULTS: Findings from 47 studies were summarized using descriptive statistics, tables, and a narrative synthesis. There is emerging support for bidirectional pathways linking caregiver-child functioning and family-child functioning, particularly for child internalizing problems. Quality assessments indicated issues with attrition and power justification. DISCUSSION: We provide a critical summary of the empirical support for the model, highlighting themes related to family systems theory and risk/resilience. We outline future directions for research on child and family well-being during COVID-19. Systematic review registration. PROSPERO [CRD42022327191].
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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 | 0.073 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".