Évaluation des effets d’une intervention infirmière sur l’adhésion thérapeutique des personnes diabétiques de type 2
Bibliographic record
Abstract
INTRODUCTION: Diabetes mellitus is a major public health problem. ContextBackground: Educational programs have been shown to be effective demonstrated their effectiveness in improving diabetes control. In Lebanon, no action has been taken to date. OBJECTIVE: The objective is to evaluate the effects of a that an educational intervention has on the therapeutic adherence of patients with type 2 diabetes on therapeutic adherence. METHOD: An experimental design was used. The sample was composed of comprised 136 patients with type 2 diabetes. They were randomized and assigned to either an experimental group, who received a nursing intervention including that involved two education sessions followed by five telephone calls within two months of the procedure, or a and in control group. Measurements were taken before the nursing intervention and three months later. RESULTS: Compared to the control group, the experimental group demonstrated a significant improvement in the level of self-efficacy levels, self-care behaviors (general diet, specific diet, physical exercise and glycemic monitoring), the application of implementing the recommendations (diet and foot care), and HbA1c levels. DISCUSSION: The results were consistent with the studies reviewed. CONCLUSION: Nursing education improves health behaviors, enhances self-efficacy, and promotes adherence in patients with type 2 diabetes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".