Building new relationships and connections in emergency management: The role of social work practitioners and human service professionals in disaster recovery
Bibliographic record
Abstract
Spurred by the climate crisis, disasters are growing in frequency and severity around the world. In Western Canada, the impacts of the 2013 floods, 2016 wildfires, and the Covid-19 pandemic have devastated communities. Social workers, and human service professionals who assist in meeting the needs of individuals, families, groups, and communities in overcoming challenges, such as education assistants, outreach workers, and child and family support workers, are increasingly involved in the mitigation, preparedness, response, and recovery phases of disasters. However, their role is not always visible or understood by emergency management professionals. Social work is a practice-based profession that responds to the needs of individuals, families, groups, and communities, and addresses barriers and injustices in organizations and society. This article shares the findings of the study “In the aftermath of the 2016 Alberta wildfires: The role of social work practitioners and human service professionals in long-term disaster recovery.” The study adopted a mixed-method research approach that recruited 140 participants to share their experiences in semi-structured interviews, an online survey, and focus groups. A thematic analysis of the data found that social workers and human service practitioners play many critical roles in disaster recovery. This includes advocacy, assessment, counselling, crisis intervention, trauma informed care, peer support, community development, providing services and programs, research, supporting first responders. Social workers can also provide valuable input into other disaster phases. For example, social workers are embedded in communities and have relationships with residents that can inform emergency plans. They provide emergency professionals insight into the needs of residents and bring collaboration skills that enhance preparedness and mitigation programs. The study findings call for greater recognition of the role of social workers and human service professionals in disasters by emergency management professionals. Implications and recommendations for social work and emergency management professionals are offered to facilitate interdisciplinary collaboration.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.027 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".