A comparison of three rural emergency homeless shelters: exploring the experiences and lessons learned in small town Ontario
Bibliographic record
Abstract
Introduction: Per capita rates of homelessness are higher in many rural communities than Canada’s largest cities, yet little attention has focused on strategies to address rural homelessness. This study compared experiences and lessons learned from three models of homeless shelters in a small town in rural Ontario: a church, motel, and warming center. Methods: Qualitative interviews were conducted with 17 individuals who stayed in or administered any of three emergency shelters that ran between 2019 and 2022. Results: Participants described challenges resulting from insufficient structure, policies, partnerships, funding, and training that led the church and motel shelters to be unsustainable. The warming center had more sustainable funding but lacked supports and had short operating hours. Several aspects of participants’ experiences were unique to the rural context, including the lack of infrastructure, precarity of services, and feelings of being surveilled and pushed out of their community. Informal supports and a sense of connection to their hometown meant most had no intention of leaving. Conclusion: The strengths of each model and lessons learned offer opportunities to improve and adapt emergency shelters to the rural context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".