Psychosocial Interventions for Emergent Adults With Type 1 Diabetes: Near-Empty Systematic Review and Exploratory Meta-Analysis
Bibliographic record
Abstract
Risk for developing mental health concerns is increased for people with diabetes. Coupled with stressors related to the transition from adolescence to adulthood, emergent adults may be in greater need of psychosocial interventions to help them cope. This review summarizes the literature on interventions used with people with diabetes aged 15–30 years on psychosocial and biological (A1C) outcomes. Core databases were searched for both published and grey research. Studies completed between January 1985 and October 2018 using any psychosocial intervention and meeting age and diabetes type requirements were selected if they included a control or comparison group and findings reported in such a way that effect size was calculable. Two authors independently extracted relevant data using standard data extraction templates. Six studies with 450 participants met the broad inclusion criteria. Sample-weighted pooling of 12 outcomes, six each on glycemic control and psychosocial status, suggested the preventive potential (d = 0.31, 95% CI 0.17–0.45) and homogeneity (χ2 [11] = 11.15, P = 0.43) of studied interventions. This preliminary meta-analysis provides some suggestion that psychosocial interventions, including telephone-based case management, individualized treatment modules, and small-group counseling interventions, may diminish burden, depression, and anxiety and enhance glycemic control among emerging adults with type 1 diabetes as they transition from adolescence to adulthood.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".