Associations between coping strategies and mental health in individuals with type 2 diabetes: Prospective analyses.
Bibliographic record
Abstract
OBJECTIVE: Individuals with type 2 diabetes are at greater risk of developing a number of mental health conditions, including depression, anxiety, and diabetes-related distress, than individuals without type 2 diabetes. Cross-sectional studies suggest that some coping strategies may increase the risk of mental health conditions in individuals with diabetes, whereas others may be protective. This study extends the cross-sectional evidence base with a prospective study. METHODS: Data were collected annually for 2 years from a community sample of 1,742 adults with type 2 diabetes. Coping strategies were measured at baseline and mental health conditions were assessed at each time point with self-report symptom measures. For comparison, cross-sectional and prospective analyses were conducted. RESULTS: Cross-sectional analyses demonstrated that task-oriented coping was negatively associated with the likelihood of each of the mental health conditions, emotion-oriented coping was positively associated with the likelihood of each condition, and avoidance-oriented coping showed no association. Prospective analyses revealed that among individuals who did not have elevated depressive symptoms at baseline, only emotion-oriented coping predicted the likelihood of developing major depression syndrome during follow-up. Similar patterns of results were observed for elevated anxiety symptoms and diabetes-related distress. CONCLUSIONS: Cross-sectional results differed from prospective results. Only emotion-oriented coping appears to play a role in the development of depressive symptoms, anxiety symptoms, and diabetes-related distress. Results underscore the importance of examining prospective associations and suggest that interventions targeting specific coping strategies might alleviate mental health problems in individuals 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".