STRATEGIES TO EDUCATE YOUNG PEOPLE WITH TYPE 1 DIABETES MELLITUS ON INSULIN THERAPY: SYSTEMATIC REVIEW
Bibliographic record
Abstract
ABSTRACT Objective: to identify evidence available in the literature on educational strategies used in the teaching of insulin therapy to children and adolescents with Type 1 diabetes mellitus. Method: systematic review undertaken in five databases, using the descriptors Insulin/therapeutic use, Patient education as topic, Diabetes mellitus type 1, Child, Infant, Adolescent and keywords, without any time limit. Primary studies on insulin therapy teaching were included, while research on insulin pumps was excluded. Results: 243 studies were identified, 13 of which were included. The results present educational strategies focused on children, adolescents and young people of up to 24 years of age, applied individually or in groups; by telephone contact or text messages by mobile phone; dramatization and educational camps; by a single professional or a multidisciplinary team. The strategies described in the analyzed studies addressed the adjustment of insulin dosages in everyday situations and education for insulin management, associated with the nutritional strategy of carbohydrate counting, diabetes education with a specific module on insulin therapy and intensive insulin use. The studies analyzed the effect of the educational intervention on several clinical and behavioral outcomes, such as glycated hemoglobin and self-efficacy. Conclusion: this review could not identify a single educational strategy able to improve metabolic and psychosocial outcomes. In most cases, nurses are the professionals responsible for the development of educational strategies focused on insulin therapy in children and adolescents with diabetes, regardless of the context in which they will be deployed. This confirms their role as educators.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".