Barriers and Motivations for the Metabolic Control of Patients with Type 2 Diabetes Assisted in a Tertiary Service in a City in Brazil
Bibliographic record
Abstract
AIMS: To evaluate the main barriers and motivations for metabolic control in patients with Diabetes Mellitus. METHODS: 101 patients assisted at the Endocrinology and Metabology service with clinical and laboratory diagnosis of Type 2 Diabetes were invited to participate in the research and answer a questionnaire developed by the authors, consisting of 65 questions, to assess barriers and treatment motivations. RESULTS: There was a predominance of females (75.2%), low education level (57.4%), income between 1.5-3.0 minimum wages (60.4%), Catholic affiliation (65.3%) and use of oral antidiabetic associated with insulin (43.6%). Patients demonstrated knowledge about the disease and the importance of maintaining good metabolic control. Most reported family support and acceptance in respect to the fact of suffering from diabetes, as well as stress related to eventual symptoms. The main motivating factors found were family relationships and personal religiosity. CONCLUSION: Ignorance and misunderstanding of some aspects of the disease, lack of support from the health system and non-acceptance of the disease are factors that interfere in the control. On the other hand, family relationships and religious engagement were considered highly motivating factors, encouranging patients to search for metabolic control and quality of life.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".