Examining Harm Reduction in a Housing First for Youth Program for Youth Experiencing Homelessness and Concurrent Disorders in a Small Canadian City
Bibliographic record
Abstract
Housing First for Youth (HF4Y) is a youth-focused adaptation of the well-established Housing First (HF) approach to housing and service provision for individuals experiencing homelessness. Given that youth homelessness is associated with an increased likelihood of substance use issues, a central tenet of the HF4Y framework is the use of a harm reduction approach to substance use. However, research on HF4Y has yet to examine how harm reduction is specifically being implemented in these settings. This study addresses this gap by examining how the principles and philosophies of harm reduction were operationalized and implemented in an HF4Y program for youth experiencing homelessness and concurrent disorders. This study was part of a larger evaluation of a 5-year HF4Y research demonstration project - the Restart Project in Kelowna, British Columbia, and Toronto, Ontario, Canada. Eight program leaders and service providers at the Kelowna site were interviewed to gather their perspectives on harm reduction delivery within the HF4Y program. Additionally, program documents and case management materials were analyzed to examine how harm reduction was operationalized and implemented through the guiding tools and resources available to staff for program delivery. Findings highlighted several ways in which harm reduction was being delivered within the HF4Y program, including working with youth to ensure safe substance use; connecting youth to services in the community; providing youth with individualized support; reducing stigma around substance use; and empowering youth who use substances. Barriers to harm reduction delivery were also identified, including a lack of low-barrier housing for youth who actively use substances and the expectations of some landlords. These findings emphasize the need for increased advocacy for housing options for youth experiencing homelessness and substance use issues and further research to address other contextual factors promoting and limiting harm reduction delivery in HF programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".