Effective lifestyle interventions to improve type II diabetes self-management for those with schizophrenia or schizoaffective disorder: a systematic review
Bibliographic record
Abstract
BACKGROUND: The prevalence of type II diabetes among individuals suffering from schizophrenia or schizoaffective disorders is more than double that of the general population. By 2005, North American professional medical associations of Psychiatry, Diabetes, and Endocrinology responded by recommending continuous metabolic monitoring for this population to control complications from obesity and diabetes. However, these recommendations do not identify the types of effective treatment for people with schizophrenia who have type II diabetes. To fill this gap, this systematic evidence review identifies effective lifestyle interventions that enhance quality care in individuals who are suffering from type II diabetes and schizophrenia or other schizoaffective disorders. METHODS: A systematic search from Medline, CINAHL, PsycINFO, and ISI Web of Science was conducted. Of the 1810 unique papers that were retrieved, four met the inclusion/exclusion criteria and were analyzed. RESULTS: The results indicate that diabetes education is effective when it incorporates diet and exercise components, while using a design that addresses challenges such as cognition, motivation, and weight gain that may result from antipsychotics. CONCLUSIONS: This paper begins to point to effective interventions that will improve type II diabetes management for people with schizophrenia or other schizoaffective disorders.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".