Quality of Diabetes Care for Individuals with Comorbid Chronic Psychotic Illness: A Sex-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Diabetes is common among individuals with chronic psychotic illness, yet they receive lower quality of diabetes care than those without psychosis. Men usually receive higher quality diabetes care than women, but whether this holds true in chronic psychotic illness populations is unknown. We aimed to determine whether quality of diabetes care differs between men and women with chronic psychotic illness. METHODS: This population-based cohort study used Ontario health administrative data to compare women and men with comorbid chronic psychotic illness and diabetes mellitus (2011-2013). The primary outcome was adherence to diabetes monitoring guidelines, defined as ≥1 retinal exam, ≥4 hemoglobin A1c (HbA1c) tests, and ≥1 dyslipidemia test during a 2-year period. Logistic regression models compared women to men to generate adjusted odds ratios (aOR) and confidence intervals (95% CI), adjusting for potential confounding variables. RESULTS: Women with chronic psychotic illness (n = 13,972) were slightly more likely to receive guideline-adherent diabetes monitoring than men (n = 12,287) (25.2% vs. 23.0%; aOR 1.20, 95% CI 1.10-1.30), including a greater likelihood of receiving ≥1 retinal exam (aOR 1.13, 95% CI 1.08-1.19) and ≥4 HbA1c tests (aOR 1.06, 95% CI 1.01-1.12). There was no difference in receipt of ≥1 dyslipidemia test (aOR 1.04, 95% CI 0.99-1.11). CONCLUSIONS: Quality of diabetes monitoring is similarly poor in women and men with chronic psychotic illness, with women receiving only marginally more optimal monitoring than men. This differs from patterns in the general population, and could have implications when designing and implementing interventions to improve diabetes care in women and men with chronic psychotic illness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".