Intervention Strategies for Prevention of Comorbid Depression Among Individuals With Type 2 Diabetes: A Scoping Review
Bibliographic record
Abstract
Background: Type 2 diabetes (T2D)-related depression has a significant impact on quality of life and leads to greater morbidity and mortality. Current educational and treatment programs for T2D rarely include a specific depression-prevention component, focusing largely on remediating depressive symptoms that have reached clinical levels. Objective: Given the vast field of research on the association between T2D and depression, and the unknown status of prevention efforts for the latter, the goal of this scoping review was to conduct a synopsis of intervention strategies specifically targeting the prevention of depression among adults with T2D. Eligibility criteria: 1) participants aged 18 and over with T2D; 2) experimental and quasi-experimental designs 3) intervention strategies seeking to prevent the onset or worsening of (non-clinical) depressive symptoms; 4) a valid measure of depressive symptoms; 5) full-text articles available in English or French. Sources of evidence: Databases including Medline, PubMed, and SCOPUS were searched between 2000 and 2018 resulting in 4219 potential articles. Charting methods: This review was conducted in-line with the current methodological framework for scoping reviews. Titles, abstract and full text articles were screened independently and in duplicate. A narrative analysis was conducted to synthesize study characteristics and the nature of intervention strategies and components. Results: Twelve studies were identified with the primary aim of preventing the incidence of depressive symptoms or improving non-clinical depression levels. Individual and group-based approaches included educational interventions incorporating diabetes self-management, problem-solving and resilience-focused approaches, emotion-targeted techniques as well as alternative interventions. Self-monitoring, home practices, and motivational interviewing were common elements. . Conclusions: This review lays the groundwork for future studies seeking to develop, validate, and improve prevention strategies targeting the diabetes-depression comorbidity. More studies over longer periods and with larger samples are needed to capture the effects of prevention efforts.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".