Physical health status of homeless Veterans in Canada: a cross-sectional study
Bibliographic record
Abstract
Introduction: Veteran homelessness is an issue gaining in visibility. Both Veterans and the homeless seem to be more susceptible to an array of physical health issues than the general population. However, very little is known about the health status of homeless Veterans in Canada. A more thorough knowledge of the physical health status of homeless Veterans could help better target services. This study has two objectives: (1) to estimate the prevalence of physical health conditions in a Canadian sample of homeless Veterans with mental illness and (2) to compare the prevalence observed in Veterans with a matched sample of homeless non-Veterans. Methods: The data come from a Canadian multi-site randomized trial, At Home/Chez Soi, that studies the effectiveness and efficiency of a Housing First program combined with a recovery-oriented approach to care. The present article is a cross-sectional analysis of baseline data. The participants are a volunteer sample of 99 homeless or precariously housed Veterans suffering from severe and persistent mental health problems and a matched sample of 99 non-Veterans. The data come from self-reported measures administered at baseline that describe chronic health conditions. Results: Veterans presented with five physical health conditions on average, the more common being dental problems, head injuries, musculoskeletal injuries, and foot problems. Both the number of conditions and the prevalence of each condition were similar to that of a matched comparison group of non-Veterans. Discussion: The number and severity of physical health conditions observed in our sample of homeless Veterans and non-Veterans suggest similar needs for physical health services in addition to housing services. Interventions targeting this population should therefore include a wide array of expertise and interdisciplinary collaboration to fit the various profiles of Veterans and non-Veterans in terms of housing, mental health, and physical health needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".