Diabetes distress, depressive, and anxiety symptoms in people with type 2 diabetes: a network analysis approach to understanding comorbidity
Bibliographic record
Abstract
Objective \nThis study aimed to explore interactions between diabetes distress, depressive, and anxiety symptoms in a cohort of adults with type 2 diabetes using network analysis. \nResearch design and methods \nParticipants (N = 1,796) were from the Evaluation of Diabetes Insulin Treatment (EDIT) study from Quebec, Canada. A network of diabetes distress symptoms was estimated using the 17 items of the Diabetes Distress Scale (DDS-17). A second network was estimated using the 17 items of the DDS-17, the 9 depressive items of the Patient Health Questionnaire (PHQ-9), and the 7 anxiety items of the Generalized Anxiety Disorder Assessment (GAD-7). Network analysis was used to identify central symptoms, clusters of symptoms, and symptoms that may bridge between diabetes distress, depressive, and anxiety symptoms. \nResults \nRegimen-related and physician-related diabetes distress symptoms were amongst the most influential (most positive connections to others) in the diabetes distress network. Feeling like a failure (depression) was identified as a potential bridge between depression and diabetes distress, being highly connected to symptoms of diabetes distress. The anxiety symptoms of worrying too much and being unable to stop worrying were found to be bridge symptoms between both anxiety and depression symptoms, and anxiety and diabetes distress symptoms, respectively. \nConclusions \nThese findings suggest individual symptoms that might be influential to the development and maintenance of diabetes distress and mental health comorbidity in diabetes and warrant further investigation. Study limitations and potential for clinical applicability are discussed.
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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 | 0.007 |
| 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 | 0.001 |
| 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".