A Strength-based Approach to Exploring Factors that Contribute to Resilience Among Children and Youth Impacted by Disaster
Bibliographic record
Abstract
Abstract Children and youth are among the most vulnerable to the detrimental effects of disaster due to their unique physical, cognitive and psychological life stage. Despite their increased vulnerability, children and youth also demonstrate resilience when faced with the adverse circumstances of disasters, and can act as important catalysts for change in their families and communities. This article discusses research conducted with eighty-three children and youth (five to seventeen years) who experienced the 2013 flood in Alberta, Canada. A mixed-methods approach was utilised. The Child and Youth Resilience Measure was used to examine the factors that contribute to resilience post-disaster, including individual, care-giver and contextual factors. In-depth qualitative interviews further examined the specific ways in which individual, caregiver and contextual factors contribute to higher levels of resilience. Findings reveal that despite numerous post-flood challenges, children and youth had higher than average levels of resilience. The findings demonstrate that high levels of resilience are associated with individual factors, specifically peer support and caregiver factors, namely caregiver psychological support. We discuss the implications of these findings for social work policy and practice, and for understanding the factors that best support the resiliency processes and overall recovery of children and youth following disaster.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".