A cross-sectional observational study of unmet health needs among homeless and vulnerably housed adults in three Canadian cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Homeless persons experience a high burden of health problems; yet, they face significant barriers in accessing health care. Less is known about unmet needs for care among vulnerably housed persons who live in poor-quality or temporary housing and are at high risk of becoming homeless. The objectives of this study were to examine the prevalence of and factors associated with unmet needs for health care in a population-based sample of homeless and vulnerably housed adults in three major cities within a universal health insurance system. METHODS: Participants were recruited at shelters, meal programs, community health centers, drop-in centers, rooming houses, and single room occupancy hotels in Vancouver, Toronto, and Ottawa, Canada, throughout 2009. Baseline interviews elicited demographic characteristics, health status, and barriers to health care. Logistic regression was used to identify factors associated with self-reported unmet needs for health care in the past 12 months. RESULTS: Of the 1,181 participants included in the analysis, 445 (37%) reported unmet needs. In adjusted analyses, factors associated with a greater odds of reporting unmet needs were having employment in the past 12 months (AOR = 1.40, 95% CI = 1.03-1.91) and having ≥3 chronic health conditions (AOR = 2.17, 95% CI = 1.24-3.79). Having higher health-related quality of life (AOR = 0.21, 95% CI = 0.09-0.53), improved mental (AOR = 0.97, 95% CI = 0.96-0.98) or physical health (AOR = 0.98, 95% CI = 0.96-0.99), and having a primary care provider (AOR = 0.63, 95% CI = 0.46-0.85) decreased the odds of reporting unmet needs. CONCLUSIONS: Homeless and vulnerably housed adults have a similar likelihood of experiencing unmet health care needs. Strategies to improve access to primary care and reduce barriers to accessing care in these populations are needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it