Prioritizing Prevention: Examining Shelter Diversion as an Early Intervention Approach to Respond to Youth Homelessness
Bibliographic record
Abstract
There is a growing movement in Canada towards youth homelessness prevention. One such response, called shelter diversion aims to move young people into safe and supportive housing as quickly as possible. The objective of this project is to assess how, and in what ways, shelter diversion operates and whether this intervention permanently or temporarily diverts youth from homelessness. Our project is grounded in principles of community-based participatory research including community/university partnerships and an advisory committee of lived-experience experts. Our team is utilizing mixed methods to capture the outcomes of diversion programs. Data collection began in September 2022 and ended in May 2024. Quantitative and qualitative data analysis is underway. Preliminary results show differences in how diversion is defined and implemented across organizations. There are also differences in staffing models and program budgets. The findings from this study will contribute to a recommendation for a national definition and adaptable program model for shelter diversion, easily accessible to support the expansion of diversion programs into youth-serving organizations across Canada. This study is the first in Canada to examine the effectiveness of shelter diversion as an early intervention strategy to prevent youth homelessness on a national scale.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".