Pandemic preparedness and response in service hub cities: lessons from Northwestern Ontario
Bibliographic record
Abstract
Purpose Responding to the needs of homeless and marginally housed persons has been a major component of the Canadian federal and provincial responses to the COVID-19 pandemic. However, smaller, less-resourced cities and rural regions have been left competing for limited resources (Schiff et al., 2020). The purpose of this paper is to use a case study to examine and highlight information about the capacities and needs of service hub cities during pandemics. Design/methodology/approach The authors draw on the experience of Thunder Bay – a small city in Northern Ontario, Canada which experienced a serious outbreak of COVID-19 amongst homeless persons and shelter staff in the community. The authors catalogued the series of events leading to this outbreak through information tracked by two of the authors who hold key funding and planning positions within the Thunder Bay homeless sector. Findings Several lessons may be useful for other cities nationally and internationally of similar size, geography and socio-economic position. The authors suggest a need for increased supports to the homeless sector in small service–hub cities (and particularly those with large Indigenous populations) to aid in the creation of pandemic plans and more broadly to ending chronic homelessness in those regions. Originality/value Small hub cities such as Thunder Bay serve vast rural areas and may have high rates of homelessness. This case study points to some important factors for consideration related to pandemic planning in these contexts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".