Experiences of quality of life among youth experiencing homelessness, mental health problems, and addictions
Bibliographic record
Abstract
Youth experiencing homelessness are often at a higher risk for developing concurrent mental health and addiction disorders. Existing interventions such as Housing First for Youth (H4FY) aim to provide youth experiencing homelessness with immediate access to safe, affordable housing as well as social inclusion and wellness supports. Furthermore, mental health interventions in the form of an integrated mental health and addictions (IMHA) service model have also been proposed to address the access barriers in place for youth experiencing homelessness, such as arbitrary exclusion criteria for substance use, long waiting periods and to further help them navigate the existing complex mental healthcare system. The Restart Project has been designed to test the effectiveness of combining both the HF4Y and IMHA services model to create a program that acts as a 'one-stop-shop' to address both housing and mental health and addictions (MHA) needs for youth. This study aimed to explore the experiences of quality of life among youth experiencing homelessness and assess the effect of the combined HF4Y and IMHA service model on quality of life (QoL) for youth aged 16-23 with concurrent MHA compared to regular access to services in the community. As a result of ongoing data collection within the Restart Project, the study includes 24 and 12 participants within the quantitative and qualitative data samples respectively. Drawing from pre-existing data from Restart, this project employed a convergent parallel mixed methods quasi-experimental design with qualitative interviews conducted at baseline from Toronto and quantitative data collected from baseline and 6 months from both the Toronto and Kelowna sites. QoL was measured using the World Health Organization Quality of Life-Brief Form (WHOQoL-BREF). Qualitative findings exhibit overall negative experiences while living in shelters for youth, as well as a number of challenges including but not limited to stigma, safety and hygiene. The quantitative results indicated a small increase over time for physical, social and environmental QoL domains across both arms with a small decrease in the intervention group for psychological QoL over 6 months. Collectively, these results highlight the harsh environments and significant challenges experienced by these youth, particularly in shelters, and suggest ways that housing programs like HF4Y can support this population.
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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 | 0.003 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".