Diabetes Education in Family: Risk Factors and Barriers to Diabetes Care in Mexican Children and Adolescents
Bibliographic record
Abstract
Objective: To determine barriers related to metabolic control and diabetes care in Mexican children and their families. Design: This was a cross-sectional study designed in two stages. First stage was an assessment of risk factors for inadequate metabolic control (HbA1c higher than ADA guidelines by age group) of diabetic children using a logistic regression model. The data sources were 91 clinical files provided by public health institutions at northwest Mexico. Second stage included the design, implementation and evaluation of an educational program (EP) based on the Medical Nutrition Therapy (MNT) and the Social Cognitive Theory (SCT), accounting for critical risk factors identified previously. Twenty five children (2 to 14 years old) with type 1 diabetes and their parents agreed to participate in the EP, which promoted healthy behavioral changes regarding diet, physical activity and medical treatment over a 4-month period. Results: Metabolic control was related to the joint effects of families low socioeconomic level and mother’s low education attainment (OR= 8.5, CI95%: 1.73, 42.16), as well as following a conventional treatment (OR= 5.0, CI95%: 1.09, 22.82). After program implementation participants’ mean glycated hemoglobin (HbA1c) decreased (9.1%±1.8% to 8.3%±2%; P=0.06). Qualitative content analysis of post-intervention interviews showed that low income, clinical inertia, and lack of social support were barriers to metabolic control of diabetes. Conclusion and Implications: Socioeconomic, educational, and healthcare factors are related to metabolic control in Mexican children with diabetes, although educational programs based on SCT can help increase self-efficacy in patients through modeling and reinforcing activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".