Comparing the Housing Trajectories of Different Classes Within a Diverse Homeless Population
Bibliographic record
Abstract
The paper presents findings from a longitudinal study identifying different classes of homeless individuals in a mid-size Canadian city based on health-related characteristics and comparing the housing trajectories of these classes 2 years later. Using data collected through in-person interviews with a sample of 329 single persons who have experienced homelessness, the paper presents results of a latent class analysis. Results found four distinct latent classes characterized by different levels of severity of health problems--i.e., a class of individuals who are "Higher Functioning" (28.7%), a second class with "Substance Abuse Problems" (27.1%), a third class with "Mental Health Substance Abuse Problems" (22.6%), and a fourth class with "Complex Physical and Mental Health Problems" (21.6%) that included having diminished physical functioning, multiple chronic physical health conditions, mental health difficulties, and in some cases substance abuse problems. Follow-up interviews with 197 of these individuals (59.9%) 2 years later showed the class of individuals with substance abuse problems experiencing the greatest difficulty in exiting homelessness and achieving housing stability. Implications of these findings for social policy development and program planning are discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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".