Promoting Older Adults’ Engagement in Disaster Recovery Through Supportive Relationships, Partnerships, and Collaboration: Perspectives of Social Work Practitioners and Human Service Professionals
Bibliographic record
Abstract
Abstract Shifting demographics—that is, the growing number of older adults in Canada and internationally—and intersecting climatic risks create a complex landscape of aging in place in disaster contexts. Some older adults are vulnerable due to their underlying health conditions and limited physical mobility. Yet they also exhibit resilience in a distinct manner, owing to their wealth of knowledge, wisdom, and ability to navigate adversities. This article elucidates potential avenues for promoting the involvement of older adults in disaster recovery efforts. Our intention is to recognize and foster older adult resilience and mitigate vulnerability by drawing upon insights from the study titled “In the Aftermath of the 2016 Alberta Wildfires: The Role of Social Work Practitioners and Human Service Professionals in Long-Term Disaster Recovery.” Employing a qualitative research approach, that study recruited 51 participants for semistructured interviews and focus groups. A thematic analysis of the collected data unveiled the pivotal roles played by social workers and human service practitioners in facilitating recovery from the 2016 wildfires in the research. Notably, three overarching themes emerged concerning the promotion of older adults’ involvement in disaster recovery: fostering supportive relationships, building partnerships and enhancing collaboration, and addressing ongoing challenges. These findings offer valuable insights into how social work practitioners and human service professionals can effectively facilitate the engagement of older adults in disaster recovery initiatives. This article emphasizes the significance of actively promoting the participation of older adults in disaster mitigation efforts, thereby fostering greater resilience within communities.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".