Refugee Children and Families During the COVID-19 Crisis: A Resilience Framework for Mental Health
Bibliographic record
Abstract
Abstract Children and families are undergoing unprecedented stress as a result of the COVID-19 pandemic, in part, due to the disruption of daily life arising from mandated social distancing protocols. As such, the purpose of the present report is to raise awareness surrounding resilience-challenging and resilience-promoting factors for refugee children and families during the COVID-19 crisis. Issues surrounding family life, parenting, and potential for family conflict are described. Also, cultural and linguistic factors are discussed, which may limit access to information about the pandemic and, accordingly, uptake of public health recommendations. Throughout our analysis, a trauma-informed framework is utilized, whereby potential for pandemic-related disruption in triggering previous traumatic stress is considered. Furthermore, using a developmental resilience framework and building upon the inherent strengths of families and children, suggestions for developing evidence-based programming and policy are reviewed. Responses should be: (1) multilevel, (2) trauma informed, (3) family focused, (4) culturally and linguistically sensitive, and (5) access oriented. The present analysis can serve as a timely guide for informing program design and policy in the context of public health, social services, mental health, health care, resettlement services, and other refugee-serving organizations.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".