Isolation to stabilization: A Housing First approach to address homelessness in Kingston, Ontario
Bibliographic record
Abstract
SETTING: Homelessness is a significant and growing public health concern across Canada. In Kingston, Ontario, the number of people experiencing chronic homelessness has more than doubled from 136 people in 2020 to 296 in 2023. INTERVENTION: An emergency shelter-in-place hotel program was established in April 2020 to provide non-congregate shelter to people experiencing homelessness and vulnerable to SARS-CoV-2 infections. Beyond preventing COVID transmission, the unintentional consequence was that a population that experienced chronic homelessness reduced drug consumption and became stable. In 2022, with increased funding from the Ministry of Health and the City of Kingston, a new Housing First program was implemented to transition individuals from homelessness to long-term stable housing. OUTCOMES: Between November 2022 and June 2023, a total of 34 clients initiated the program. Of these clients, 10 completed the program and were successfully housed, 10 remained active participants, and 14 were discharged before completion. Strengths and challenges were identified. Diverse services provided to meet the population's needs and strong collaborations with various community partners were facilitating factors. Inadequate external resources, a lack of evening and prosocial activities, and outside peers (not part of the program) who influenced recovery plans were identified as challenges. IMPLICATIONS: This program illustrates that simultaneously integrating housing, community building, mental health, and addiction services is possible and provides an innovative way to stabilize this vulnerable population of people experiencing homelessness. Results from this program and the knowledge generated through implementation are being used to further scale up the program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".