Impact de divers types d’hébergement chez les personnes en situation d’itinérance au Québec
Bibliographic record
Abstract
OBJECTIVES: This study aimed to identify variables and profiles of individuals experiencing homelessness associated with service use and satisfaction, suicidal behaviors, residential stability, quality of life and adequacy of needs, and to evaluate whether one type of accommodation (shelters, transitional, permanent housing) is more closely associated with better service use and more positive impacts. METHOD: The study is based on the synthesis of 10 articles recently published in international anglophone journals, in which a cohort of individuals experiencing homelessness responded at a 12-month interval (T0=455, T1=270) to a questionnaire. Regression and cluster analyses were produced based on the Gelberg model, classifying associated variables into predisposing, enabling and need factors. A mixed method design adapted from the Maslow model dealing with unmet needs was also used. RESULTS: At T1, 81% of the individuals who were living in permanent housing at T0 still lived there 12 months later; 54% of those who were in transitional housing had gained access to permanent housing, while 64% of shelters users were still using them at T1. Overall, individuals living in permanent housing had less health and social problems, were more followed by a case manager and a physician, and presented better health outcomes including fewer emergency department visits and hospitalizations. CONCLUSION: The study reinforces the promotion of consolidation of permanent supported housing for individuals experiencing homelessness. Transitional housing is nevertheless recommendable for individuals with fewer or less complex health problems, having facilitated access to permanent housing in the study.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".