Predictors of homelessness among vulnerably housed adults in 3 Canadian cities: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Homelessness is a major concern in many urban communities across North America. Since vulnerably housed individuals are at risk of experiencing homelessness, it is important to identify predictive factors linked to subsequent homelessness in this population. The objectives of this study were to determine the probability of experiencing homelessness among vulnerably housed adults over three years and factors associated with higher risk of homelessness. METHODS: Vulnerably housed adults were recruited in three Canadian cities. Data on demographic characteristics, chronic health conditions, and drug use problems were collected through structured interviews. Housing history was obtained at baseline and annual follow-up interviews. Generalized estimating equations were used to characterize associations between candidate predictors and subsequent experiences of homelessness during each follow-up year. RESULTS: Among 561 participants, the prevalence of homelessness was 29.2 % over three years. Male gender (AOR = 1.59, 95 % CI: 1.14-2.21) and severe drug use problems (AOR = 1.98, 95 % CI: 1.22-3.20) were independently associated with experiencing homelessness during the follow-up period. Having ≥3 chronic conditions (AOR = 0.55, 95 % CI: 0.33-0.94) and reporting higher housing quality (AOR = 0.99, 95 % CI: 0.97-1.00) were protective against homelessness. CONCLUSIONS: Vulnerably housed individuals are at high risk for experiencing homelessness. The study has public health implications, highlighting the need for enhanced access to addiction treatment and improved housing quality for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".