Social-Ecological Factors Associated With Higher Levels of Resilience in Children and Youth After Disaster: The Importance of Caregiver and Peer Support
Bibliographic record
Abstract
Children and youth are among the most vulnerable to the devastating effects of disaster due to the physical, cognitive, and social factors related to their developmental life stage. Yet children and youth also have the capacity to be resilient and act as powerful catalysts for change in their own lives and wider communities following disaster. Specific factors that contribute to resilience in children and youth, however, remain relatively unexplored. This article examines factors associated with high levels of resilience in 100 children and youth aged 5- to 18-years old who experienced the 2016 Fort McMurray, Alberta wildfire. A mixed-methods design was employed combining quantitative and qualitative data. Quantitative data was obtained from the Children and Youth Resilience Measure (CYRM-28) which measured individual, caregiver, and context factors influencing resilience processes among the participants. Qualitative data was collected through semi-structured interviews to gain further insight into the disaster experiences of children and youth. Quantitative findings reveal higher than average levels of resilience among the participants compared to normative scores. Qualitative findings suggest high levels of resilience were associated with both caregiver factors (specifically physical caregiving), and individual factors (primarily peer support). We discuss how physical caregiving and peer support during and after the wildfire helped mitigate the negative effects of disaster, thus bolstering children and youth's resilience. Implications for understanding the specific social-ecological factors that facilitate and support resiliency processes and overall recovery of children and youth following disaster are also discussed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".