Factors related to diabetes distress among adolescent patients with type 1 diabetes： a Meta-analysis
Bibliographic record
Abstract
BackgroundDiabetes distress is highly prevalent and has adverse impacts in adolescent patients with type 1 diabetes. However， the related factors of diabetes distress in adolescents with type 1 diabetes are not clear.ObjectiveTo identify the factors associated with diabetes distress in adolescents with type 1 diabetes using Meta-analysis， and to provide a scientific evidence for effective prevention and improvement of diabetes distress in adolescents with type 1 diabetes.MethodsOn December 1， 2022， a computerized search was conducted on databases including PubMed， Cochrane Library， Web of Science， Embase， CNKI， Wanfang Data， VIP and China Biomedical Literature Database， and studies relevant to diabetes distress in adolescents with type 1 diabetes were systematically included. Quality assessment of cross-sectional and cohort studies was carried out using criteria defined by the Agency for Healthcare Research and Quality （AHRQ） and Newcastle-Ottawa Scale （NOS）. Then the included studies were pooled in a Meta-analysis using Revman 5.3.ResultsA total of 22 studies were included， involving 6 442 adolescents with type 1 diabetes. Meta-analysis denoted that the occurrence of diabetes distress among adolescents with type 1 diabetes was correlated with age （r=0.094，95% CI： 0.042~0.145）， HbA1c （r=0.291， 95% CI： 0.248~0.335）， trait anxiety （r=0.585， 95% CI： 0.526~0.639）， depressive symptoms （r=0.635， 95% CI： 0.590~0.676）， resilience （r=-0.410， 95% CI： -0.528~-0.276） and parents' diabetes distress （r=0.462， 95% CI： 0.421~0.501）.ConclusionFactors including age， HbA1c， trait anxiety， depressive symptoms， resilience and parents' diabetes distress are correlated with diabetes distress in adolescents with type 1 diabetes. ［Funded by Sichuan Science and Technology Program （number， 24KJPX0034）］
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.066 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".