COMPARATIVE ANALYSIS OF MANAGEMENT MODELS FOR DIABETES PROGRAMS IN BRAZIL, THE UNITED STATES AND CANADA
Bibliographic record
Abstract
Diabetes Mellitus (DM) is a complex metabolic condition caused by different causes, characterized by a deficiency or inability of the body to use insulin properly.This study sought to compare the health systems of Brazil, Canada and the United States of America, focusing on health management, prevention of the disease studied, guidance and care for patients with DM.The study is presented as an integrative literature review, composed of scientific articles in electronic media on the VHL platform that were published between 2018 and 2023, in Portuguese and English, where the purpose is to collect and synthesize research results on a defined topic or question in a systematic and orderly manner that contributes to a better understanding of the subject under study.Data analysis enabled the classification of two (2) thematic categories, as follows: Category A: Telemedicine technology applied in the Unified Health System in the care of people with diabetes mellitus; and Category B: Scarcity of evidence and new technologies in the 3 countries surveyed.This study reinforces the importance of telemedicine as a valuable tool for overcoming access barriers and improving care in the context of diabetes mellitus in the SUS, as well as pointing out the urgent need for investment in research, continuing education and careful implementation of new technologies to improve the quality of life and health management of this population.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".