Diabetes Distress, Depressive Symptoms, and Anxiety Symptoms in People With Type 2 Diabetes: A Network Analysis Approach to Understanding Comorbidity
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to explore interactions between individual items that assess diabetes distress, depressive symptoms, and anxiety symptoms in a cohort of adults with type 2 diabetes using network analysis. RESEARCH DESIGN AND METHODS: Participants (N = 1,796) were from the Montreal Evaluation of Diabetes Treatment (EDIT) study from Quebec, Canada. A network of diabetes distress was estimated using the 17 items of the Diabetes Distress Scale (DDS-17). A second network was estimated using the DDS-17 items, the nine items of the Patient Health Questionnaire (PHQ-9), and the seven items of the Generalized Anxiety Disorder Assessment (GAD-7). Network analysis was used to identify central items, clusters of items, and items that may act as bridges between diabetes distress, depressive symptoms, and anxiety symptoms. RESULTS: Regimen-related and physician-related problems were among the most central (highly connected) and influential (most positive connections) in the diabetes distress network. The depressive symptom of failure was found to be a potential bridge between depression and diabetes distress, being highly connected to diabetes distress items. The anxiety symptoms of worrying too much, uncontrollable worry, and trouble relaxing were identified as bridges linking both anxiety and depressive items and anxiety and diabetes distress items, respectively. CONCLUSIONS: Regimen-related and physician-related diabetes-specific problems may be important in contributing to the development and maintenance of diabetes distress. Feelings of failure and worry are potentially strong candidates for explaining comorbidity. These individual diabetes-specific problems and mental health symptoms could hold promise for targeted interventions for people with type 2 diabetes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 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".