HEALTH STATUS AND SERVICE USE IN HOMELESS INDIVIDUALS WITH MENTAL ILLNESS: CONSISTENCY BETWEEN SELF-REPORT AND ADMINISTRATIVE HEALTH RECORDS IN THE AT HOME/CHEZ SOI MULTI-SITE TRIAL
Bibliographic record
Abstract
Objective Homeless individuals with poor health frequently use healthcare services. However, studies using self-reported data may be subject to biases. We examined health status, healthcare and drug use among mentally ill homeless individuals, comparing self-report and administrative data claims to estimate the degree of agreement between the two sources. Methods Baseline survey data from 100 participants of the Winnipeg site of the Mental Health Commission of Canada's At Home/Chez Soi research project were linked to deidentified administrative health records stored in the Repository at the Manitoba Centre for Health Policy. Demographic characteristics, homelessness histories and health service use were analysed, as well as disease status for asthma, hypertension, arthritis and diabetes (using previously validated definitions). Participants were similarly classified using their survey responses. The degree of agreement between the two data sources was evaluated using cross-tabulations and the κ statistic. Results There was 100% linkage of surveyed homeless people with the Repository data. In 1 year, 97% of participants had at least one ambulatory physician visit, with an age- and sex-adjusted rate of 14.82 per person-year (Manitoba rate=4.99 per person-year). 34% had an inpatient hospitalisation (adjusted hospital separation rate=491 per thousand person-years vs the Manitoba rate of 137 per thousand person-years). 95% filled at least one prescription, with 65% of drugs targeting the nervous system (majority were psycholeptics). The degree of agreement between the data sources ranged from a κ of 0.27 for arthritis to 0.57 for hypertension. Individuals were more likely to be classified as having one of the four conditions based on the administrative data than on the survey data. Conclusions Compared with the general population, participants had high health service use, and high prescription drug use. There was poor to moderate agreement between the two data sources. Researchers studying homeless persons with mental illness should consider using multiple data sources to estimate disease prevalence and health service use.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".