Social Work Practitioners and Human Service Professionals in the 2016 Alberta (Canada) Wildfires: Roles and Contributions
Bibliographic record
Abstract
Abstract The 2016 Alberta wildfires resulted in devastating human, economic and environmental impacts. Social work practitioners and human service professionals are increasingly involved in disaster contexts yet there remains a pressing need to better understand their professional role and contributions. The wildfire resulted in the mobilisation and engagement of social work practitioners and human service professionals to meet the needs of individuals, families, groups and affected community members. Research was undertaken to identify the roles and responsibilities of social work practitioners and human service professionals in the context of the wildfires in Fort McMurray, AB, Canada. Forty social work practitioners and human service professionals were interviewed about their direct experience in the provision of social services in the context of the 2016 wildfire. This article shares the findings based on four themes: social work practice in disaster contexts; social work role in disaster management; building capacity and advocacy, wellness and self-care. Implications and recommendations discuss the need to enhance understandings of the roles and contributions of social work practitioners and human service professionals in disasters with a particular focus on long-term disaster recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".