Homelessness in the district of Nipissing of Ontario, Canada before, at the onset and during the COVID-19 pandemic: a trend analysis (2018–2021)
Bibliographic record
Abstract
BACKGROUND: Homelessness is a growing social concern experienced across Canada. In Ontario, specifically in the District of Nipissing, the issue has become larger with an increasing number of homeless individuals. Previous research has described the demographic composition of the homeless population both in the Nipissing District of Ontario and in the city of North Bay. However, no studies have examined homelessness in this region before, at the beginning and during the COVID-19 pandemic. This research investigates structural and individual-level barriers and factors that are associated with becoming homeless or remaining homeless. METHODS: This study utilizes data from the 2018 (n = 147), 2020 (n = 254), and 2021 (n = 207) homelessness enumeration surveys, conducted in the District of Nipissing, Ontario by the District of Nipissing Social Services Administration Board. This study employs quantitative, descriptive analyses to examine trends and socio-demographic variations in the reasons of homelessness, barriers to housing, episodic and chronic homelessness before, at the beginning, and during the COVID-19 pandemic. RESULTS: The results revealed a rise in the proportion of male homeless (57% vs. 64%), and first-time homelessness among those aged 35-44 (3%, vs. 15%) and 55-64 (1% vs. 5%) at the onset and during the pandemic. The sleep location of homeless individuals was also influenced by the pandemic, where emergency shelter use dropped to half during 2020-2021(33% vs. 17%), while the use of locations (hotel/motels) where proper pandemic protocols and social distancing were possible increased sharply from 2 to 12% of homeless individuals. With the onset of the pandemic, chronic homelessness and one-episodic homelessness increased, suggesting that individuals are becoming homeless and staying homeless for prolonged periods. The barriers to housing during the pandemic were largely addiction, substance use and the inaccessibility of safe and secure rental units, while the corresponding barriers before the pandemic were mainly low income. CONCLUSIONS: The rise in male homelessness, age at first-time homelessness and interpersonal conflict causing homelessness at the onset and during the pandemic suggest that policy makers need to focus on providing homeless supports to these groups of homeless populations at the time of pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".