Efecto de un modelo de apoyo telefónico en el auto-manejo y control metabólico de la Diabetes tipo 2, en un Centro de Atención Primaria, Santiago, Chile
Bibliographic record
Abstract
BACKGROUND: Telephone based self-management support may improve the metabolic control of patients with type2 (DM2) diabetes if it is coordinated with primary care centers, if telephone protocols and clinical guidelines are used and if it is provided by nurses trained in motivational interviewing. AIM: To assess the efficacy of a tele-care self-management support model (ATAS) on metabolic control of patients with DM2 attending primary care centers in a low income area in Santiago, Chile. MATERIAL AND METHODS: Two primary care centers were randomly assigned to continue with usual care (control group, CG) or to receive additionally 6 telecare self-management support interventions (IG) during a 15 month period. Glycosylated hemoglobin (HbA1c) was used to measure metabolic control of DM2; the "Summary of Diabetes Self-care Activities Measure" and the "Spanish Diabetes Self-efficacy" scale were used to measure self-management and self efficacy, respectively. Changes in the use of health services were also evaluated. RESULTS: The IG maintained its HbA1c level (baseline and final levels of 8.3 +/- 2.3% and 8.5 +/- 2.2% respectively) whereas it deteriorated in the CG (baseline and final levels of 7.4 +/- 2.3 and 8.8 +/- 2.3% respectively, p < 0.001). The perception of self-efficacy in the IG improved while remaining unchanged in the CG (p < 0.001). Adherence to medication, physical activity and foot care did not change in either group. In the IG, compliance to clinic visits increased while emergency care visits decreased. CONCLUSIONS: The ATAS intervention, in low income primary care centers, significantly increased the probability of stabilizing the metabolic control of patients with DM2 and improved their use of health services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".