Prioritizing psychosocial services for children, youth and families postdisaster
Bibliographic record
Abstract
Purpose This article explores the community recovery and resilience element of “building back better” (BBB) through the perspectives and experiences of community influencers who provided psychosocial supports after the 2013 floods in southern Alberta, Canada. Design/methodology/approach The Alberta Resilient Communities (ARC) project adopted a community-based research methodology to examine the lived realities of children, youth, families and their communities postflood. In-depth semistructured interviews were conducted with 37 community influencer participants representing a range of organizations including not-for-profit agencies, community organizations, social service agencies and government departments. Findings The findings were drawn from the interviews held with community influencers in flood-affected communities. Major themes include disaster response challenges, insufficient funding for long-term disaster recovery, community partnerships and collaborations and building and strengthening social capital. Practical implications Findings demonstrate the need to build better psychosocial services, supports and resources in the long term to support community recovery and resilience postdisaster for children, youth and families to “build back better” on a psychosocial level. Social implications Local social service agencies play a key role in the capacity of children, youth and families to “build back better” postdisaster. These organizations need to be resourced and prepared to respond to psychosocial needs in the long term in order to successfully contribute to postdisaster recovery. Originality/value The findings illustrate that adopting a psychosocial framework for disaster recovery can better inform social service disaster response and long-term recovery plans consistent with the BBB framework. Implications for social service agencies and policymakers interested in fostering postdisaster community recovery and resilience, particularly with children and youth, are presented.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".