Refugee Children and Families During the COVID-19 Crisis: A Resilience Framework for Mental Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it