Factors associated with homeless experiences amid the COVID-19 pandemic in the Nipissing District, Ontario, Canada
Bibliographic record
Abstract
Canadian homelessness is an ongoing issue, especially in the Nipissing District, Ontario, where agencies work to support those in need. However, these efforts were challenged with the sudden onset of the COVID-19 pandemic. Drawing on the Cycle of Homelessness model, this study examines sociodemographic factors associated with homeless experiences during the pandemic. Using data from the 2021 (n = 207) Nipissing District homeless enumeration survey and employing bivariate and multivariate binary logistic analyses, this study examined sociodemographic factors associated with reasons of homelessness, barriers to housing loss and experiences of chronic and episodic homelessness during the pandemic. The results showed a significant sociodemographic variation in the experiences of the homeless population during the COVID-19 pandemic. Those over the age of 35 versus their younger counterparts were more likely (43.7%) found in emergency shelters. Multivariate findings indicated that females experienced housing/financial loss and interpersonal/family issues, directly causing homelessness, 2.2 and 2.5 times more than males, respectively. Welfare recipients were more likely to experience health-related reasons for housing loss (Odds Ratio (OR): 2.8), chronic homelessness (OR: 3.3), addiction (OR: 2.9), and mental health-related barriers to housing (OR: 4.1). Those aged 25-34, 25-44, and 45+ were 7.9, 4.9, and 5.1 times more likely to face chronic homelessness. Conclusions: Welfare recipients are more at-risk of health-related housing loss, addiction, and mental health barriers to housing, and chronic homelessness. This could be attributed to poor public planning and policies that put people in marginal economic and housing circumstances, especially during the pandemic. Therefore, policy reform is required to address the main barriers in eliminating homelessness.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".