Building Emergency Response Capacity: Multi-Career-Stage Social Workers’ Engagement with Homeless Sector during the First Two Waves of COVID-19 in Halifax, Nova Scotia, Canada
Bibliographic record
Abstract
The dramatic increase of global extreme events (e.g., natural, technological, and willful hazards) propels social workers to be equipped with emergency response capacity, supporting affected individuals, families, and communities to prepare, respond, and recover from disasters. Although social workers have historically been engaged in emergency response, social work curriculum and professional training remain slow to adapt, jeopardizing their capacity to support the vulnerable and marginalized populations, who have always been disproportionately affected by extreme events. In response to this deficit, this article utilizes a critical reflection approach to examine three social workers' (a senior faculty, a junior faculty, and a social work student) interventions and challenges in their emergency response to persons experiencing homelessness (PEHs) during the first two waves of COVID-19 in Halifax, Nova Scotia, Canada (March 2020 to March 2021). The cross-career-stage reflections and analyses exhibit these three social workers' COVID-19-specific emergency response efforts: a top-down advocacy effort for social development and policy, a bottom-up cognitive effort to comprehend the community's dynamics, and a disaster-driven self-care effort. These three types of effort demonstrate a greater need for social work education and professional training, to develop more disaster-specific components to contribute to building the emergency response capacity of the next generation of social workers through in-classroom pedagogical enhancement and on-site field education training, better supporting PEHs and other vulnerable and marginalized groups living in the diverse context of extreme events in Canada and internationally.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".