Adherence to Diabetes Medication in Individuals with Schizophrenia: A Systematic Review of Rates and Determinants of Adherence
Bibliographic record
Abstract
INTRODUCTION: Despite the importance of medication adherence for the effective treatment of type II diabetes mellitus (T2DM), little research has examined adherence with diabetes medication treatment in schizophrenia. The purpose of this systematic review was to: 1) evaluate rates of adherence and determinants of adherence with medication for T2DM in individuals with schizophrenia; and, where possible, 2) examine the relationship between medication adherence and glycemic control. METHODS: Studies were included if they presented information on dosing regimens and adherence or compliance rates for T2DM and included samples where at least 50% of the participants were individuals with schizophrenia. RESULTS: Six studies were included in this review that predominantly examined men over the age of 50 years. Studies confirmed that many individuals with schizophrenia were not adhering to their diabetes medication as adherence rates ranged from 51-85%. Two studies that compared medication adherence in individuals with and without schizophrenia found those with the mental illness had higher rates of adherence. One study reported that blood glucose control levels were not statistically different between those who did and did not adhere to their medication, indicating more research is necessary in this area. Factors that improved adherence included disease and medical service and medication-related factors. CONCLUSIONS: Interventions to increase diabetes medication adherence in schizophrenia need to address disease and medical service and medication-related factors. Further research needs to examine diabetes medication adherence in women, younger individuals, and those recently diagnosed with diabetes as these individuals have been underrepresented in the literature.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".