Barriers to appropriate diabetes management among homeless people in Toronto.
Bibliographic record
Abstract
BACKGROUND: Homeless people are more likely to have chronic medical conditions and to encounter barriers to health care than the general population. In this study we identify barriers to appropriate disease management among homeless adults with diabetes mellitus in Toronto. METHODS: People with diabetes were surveyed at homeless shelters in Toronto. Information was obtained on demographic characteristics, diabetes history, access to health care, substance abuse and mental illness. Participants' descriptions of the difficulties they experienced in managing their diabetes were analysed qualitatively. Hemoglobin A1c levels were used to assess adequacy of glycemic control. RESULTS: Fifty people completed the survey (response rate 83%). Of the respondents 82% were male and 64% were white. Type 2 diabetes had been diagnosed in 86%, with 62% of all participants taking oral agents alone and 28% taking insulin alone. Overall, 72% of the participants reported experiencing difficulties managing their diabetes: the most common were related to diet (type of food at shelters and inability to make dietary choices, reported by 64%) and scheduling and logistics (inability to get insulin and diabetic supplies when needed and inability to coordinate medications with meals, reported by 18%). Although alcohol use, cocaine use and mental health problems were common, few respondents cited these issues as barriers to diabetes management. According to Canadian Diabetes Association guidelines, glycemic control was inadequate in 44% of the people tested. INTERPRETATION: In Toronto, most homeless adults with diabetes report difficulties managing their disease, and poor glycemic control is common.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".