An Assessment of Quality of Life Among Children with Type 1 Diabetes Mellitus:A Cross-Sectional Study
Bibliographic record
Abstract
Type 1 Diabetes Mellitus (T1DM) is a chronic metabolic disorder that significantly affects children’s physical, emotional, and social well-being. Quality of life (QoL) is an important outcome for pediatric patients, reflecting their ability to manage the disease while maintaining normal daily functioning, social participation, and psychological health. This cross-sectional study aimed to assess the quality of life among children with T1DM and to identify clinical, psychosocial, and environmental factors influencing their well-being. A quantitative approach was employed, collecting data from 150 children aged 6–18 years using the Pediatric Quality of Life Inventory (PedsQL 4.0), supplemented by clinical records and structured parent interviews. Data analysis included descriptive statistics, correlation analysis, and multiple regression to determine predictors of quality of life across physical, emotional, social, and school domains. Results indicated that children with good glycemic control, longer disease duration, strong family and peer support, and access to school accommodations reported higher quality of life, whereas poor glycemic control, high parental stress, and lack of social support were associated with lower QoL. The findings highlight the critical need for integrated care approaches that combine medical management with psychosocial support and educational interventions to optimize outcomes for pediatric T1DM patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".