Resilience to hazards overlapping a pandemic: A shelter resource stabilization model
Bibliographic record
Abstract
COVID-19 and the resulting financial impacts had budget and workforce implications for organizations involved in emergency shelter provision. To address distancing and sanitation protocols as well as virus transmission and vaccination rates, shelter supplies, facility modifications, and staffing adjustments were needed. As funding and authorizations to implement public health guidance and stabilize the workforce expired, emergency managers had to determine whether to continue pandemic protocols. In order to understand these relationships, we conducted a workshop in September 2021 with 137 emergency managers, public health leaders, and other government and nonprofit practitioners from 20 states, the U.S. Virgin Islands, and Canada to identify resource reallocation strategies utilized for sheltering from post-lockdown to post-vaccine periods of the COVID-19 pandemic. We applied a fiscal recovery framework to explore how these operational changes were influenced by and have implications for fiscal and policy support as well as operational adaptability. Results show that as fiscal and policy support waned, some pandemic protocols were suspended, thereby shifting and reducing human and facility resource needs. Distancing protocols benefited from improvements in masking, vaccine, and testing availability without consistent mandates, but non-congregate shelter provision was reduced as authorizations and funding expired. Modified service contracts and increased utilization of special needs registries can realign resources with health and safety needs. Based on participant responses, we developed a Retractable Stabilization Model to indicate how shelter resources can be reallocated and supported by future policy interventions to provide a range of emergency sheltering options during pandemics and overlapping hazards.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".