Characteristics of People with Type I or Type II Diabetes with and without a History of Homelessness: A Population-based Cohort Study
Bibliographic record
Abstract
Abstract Introduction Homelessness poses unique barriers to diabetes management. Population-level data on the risks of diabetes outcomes among people experiencing homelessness are needed to inform resource investment. The aim of this study was to create a population cohort of people with diabetes with a history of homelessness to understand their unique demographic and clinical characteristics and improve long-term health outcomes. Methods Ontario residents with diabetes were identified in administrative hospital databases between 2006 and 2020. A history of homelessness was identified using a validated algorithm. Demographic and clinical characteristics were compared between people with and without a history of homelessness. Propensity score matching was used to create a cohort of people with diabetes experiencing homelessness matched to comparable non-homeless controls. Results Of the 1,455,567 patients with diabetes who used hospital services, 0.7% (n=8,599) had a history of homelessness. Patients with a history of homelessness were younger (mean: 54 vs 66 years), more likely to be male (66% vs 51%) and more likely to live in a large urban centre (25% vs 7%). Notably, they were also more likely to be diagnosed with mental illness (49% vs 2%) and be admitted to a designated inpatient mental health bed (37% versus 1%). A suitable match was found for 5219 (75%) people with documented homelessness. The derived matched cohort was balanced on important demographic and clinical characteristics. Conclusion People with diabetes experiencing homelessness have unique characteristics that may require additional supports. Population-level comparisons can inform the delivery of tailored diabetes care and self-management resources.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".