Symptoms of diabetes distress, depression, and anxiety in people with type 2 diabetes: identifying central and bridge symptoms using network analysis
Bibliographic record
Abstract
Introduction People with diabetes are vulnerable to diabetes-related distress and are more likely to experience depressive and anxiety symptoms than the general population. Diabetes distress, depressive, and anxiety symptoms also tend to commonly co-occur. Objectives This study aimed to apply network analysis to explore the associations between diabetes distress, depressive, and anxiety symptoms in a cohort of adults with type 2 diabetes. Methods Data were from the baseline (2011) assessment of the Evaluation of Diabetes Insulin Treatment (EDIT) study (N = 1,796; 49% female; mean age = 60, SD = 8) from Quebec, Canada. A first network using the 17 items of the diabetes distress scale (DDS-17) was estimated. A second network was estimated using the 17 items of the DDS-17, the 9 depressive items of the PHQ-9, and the 7 anxiety items of the GAD-7. Symptom centrality, network stability, and bridge symptoms were examined. Results Regimen-related and physician-related distress symptoms were amongst the most central (highly connected) in the diabetes distress network. Worrying too much (anxiety), Not feeling motivated to keep up diabetes self-management (diabetes distress), and Feeling like a failure (depression) were the most central symptoms in the combined network. Feeling like a failure (depression) was highly connected to diabetes distress symptoms, representing a potential bridge between diabetes distress and depression. Conclusions Identifying central and bridge symptoms may provide new insights into diabetes distress, depressive, and anxiety symptom maintenance and comorbidity in people with type 2 diabetes. Disclosure No significant relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| 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".