Understanding subjective quality of life in homeless and vulnerably housed individuals: The role of housing, health, substance use, and social support
Bibliographic record
Abstract
Much of the extant research on quality-of-life (QoL) in homelessness has focused narrowly on health-related QoL. Far less is known about broader subjective quality-of-life (SQoL). The purpose of this study was to examine levels and predictors of SQoL among homeless and vulnerably housed individuals in a 4-year longitudinal study. Data were from the Health and Housing in Transition (HHiT) study, a prospective cohort study of 1190 individuals from three Canadian cities who were homeless or vulnerably housed. SQoL was measured using a scale designed specifically for this population. Predictor variables were time-varying indicators of housing status, substance use, and social support as well as baseline measures of physical and mental health problems. Generalized estimating equation (GEE) analysis examined these predictors of SQoL, adjusting for baseline age, gender, ethnicity, employment, income, and city of residence. Participants at baseline were 18 to 80 years old (M = 42.2; SD = 10.6), 66% male, 63% White, 60% unemployed, and 50% were currently homeless. Most areas of SQoL tended toward slight satisfaction, with dissatisfaction concerning finance and employment as notable exceptions. Demographic variables were not associated with SQoL. Homelessness and self-reported presence of 3+ chronic health conditions, mental health problems, and problematic substance use, as well as lower perceived social support, were independently associated with lower SQoL. SQoL shows variation across different life areas for homeless and vulnerably housed individuals. In order to improve SQoL in this population, it is important to address poverty, employment, housing challenges, mental and physical health problems, and substance use as well as increase social supports.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".