Variables associated with higher community integration among permanent supportive housing residents
Bibliographic record
Abstract
Few studies have used a framework for exploring a broad range of resident characteristics, housing, and service use features that might increase community integration among permanent supportive housing (PSH) residents who have lived in scattered-site or single-site PSH for 6 months to 5+ years. This study is original in identifying predisposing, enabling, and need factors associated with higher community integration among PSH residents. PSH residents from Quebec (Canada) were recruited through 25 housing organizations between January 2020 and April 2022. Structured interviews using numerous standardized scales were conducted, each lasting about 90 minutes. Based on the Gelberg-Andersen Behavioral Model, independent variables, measured mostly within 12 months of the interview, were categorized into predisposing, need, and enabling factors. Multivariate linear regression analysis was produced on community integration. Those experiencing moderate to severe psychological distress and having more unmet needs had lower community integration scores. Conversely, residents in single-site PSH located in neighborhoods with good physical conditions and high collective effectiveness, and those who received more outpatient services showed better community integration. Enhancing intergovernmental collaboration to develop high-quality PSH in well-maintained neighborhoods, focusing on socialization and community participation, especially for scattered-site PSH residents, may contribute to improved community integration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".