Employment and Income of People Who Experience Mental Illness and Homelessness in a Large Canadian Sample
Bibliographic record
Abstract
OBJECTIVES: Research suggests that homeless people with mental illness may have difficulty obtaining employment and disability benefits. Our study provides a comprehensive description of sources of income and employment rates in a large Canadian sample. METHODS: Participants (n = 2085) from the 5 sites of the At Home/Chez Soi study were asked about their income, employment, and desire for work during the pre-baseline period. The proportion of participants employed, receiving government support, and relying on income from other activities were compared across sites, as were total income and income from different sources. Generalized linear models were used to identify participant characteristics associated with total income. RESULTS: Unemployment ranged from 93% to 98% across 5 sites. The per cent of participants who wanted to work ranged from 61% to 83%. Participants relied predominantly on government assistance, with 29.5% relying exclusively on welfare, and 46.2% receiving disability benefits. Twenty-eight per cent of participants received neither social assistance nor disability income. Among the 2085 participants, 6.8% reported income from panhandling, 2.1% from sex trade, and 1.2% from selling drugs. Regression models showed that income differed significantly among sites and age groups, and was significantly lower for people with psychotic illnesses. CONCLUSION: These results suggest that homeless people with mental illness are predominantly unemployed, despite expressing a desire to work. In Canada, this group relies predominantly on welfare, but has access to disability benefits and employment insurance. These findings highlight the importance of developing effective interventions to support employment goals and facilitate access to benefits.
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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 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".