Child and Family Outcomes Following Pandemics: A Systematic Review and Recommendations on COVID-19 Policies
Bibliographic record
Abstract
OBJECTIVE: A systematic review of mental health outcomes and needs of children and families during past pandemics was conducted based on the PRISMA protocol. The objectives were to evaluate the quality of existing studies on this topic, determine what is known about mental health outcomes and needs of children and families, and provide recommendations for how COVID-19 policies can best support children and families. METHODS: Seventeen studies were identified through a search of PsycINFO, PubMed, Scopus, Web of Science, and Google Scholar. RESULTS: Studies examining child outcomes indicate that social isolation and quarantining practices exert a substantial negative impact on child anxiety, post-traumatic stress disorder, and fear symptoms. Potential risk factors such as living in rural areas, being female, and increasing grade level may exacerbate negative mental health outcomes for children. Studies examining parental and family outcomes indicate that parents experience high stress, anxiety, and financial burden during pandemics. The age of the parent and family socioeconomic status (SES) appeared to mitigate negative outcomes, where older parents and higher SES families had lower rates of mental health problems. Parents' fear over the physical and mental health of their children, concerns over potential job loss and arranging childcare contributes to elevated stress and poorer well-being. CONCLUSIONS: Findings from this review suggest current gaps in COVID-19 policies and provide recommendations such implementing "family-friendly" policies that are inclusive and have flexible eligibility criteria. Examples include universal paid sick leave for parents and financial supports for parents who are also frontline workers and are at an elevated risk for contracting the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".