Management of Type 1 diabetes in a limited resource context: A study of the diabetes research education and management trust model in Nagpur, Central India
Bibliographic record
Abstract
Background/Objective: Diabetes Research Education and Management (DREAM) Trust (DT) is a charitable organisation that offers free insulin and healthcare to children and youth with type 1 diabetes (T1D) in central India. We systematically describe DT's model of care and evaluate medical and sociodemographic factors influencing glycaemic control in this resource-poor setting. Methods: Study of DT patients diagnosed with T1D <16 years old and followed at DT ≥1 year. Participants completed an interview, retrospective chart review and prospective haemoglobin A1c (HbA1c) measurements. Uni- and multi-variate linear regressions determined factors associated with HbA1c. Percentage of underweight patients (as proxy for glycaemic control) was compared at presentation to DT versus time of interview. Results: A total of 102 DT patients (51% female) completed the interview and chart review. 74 had HbA1c measured. Median HbA1c was 10.4% (90.2 mmol/mol). In multivariate regression, higher HbA1c was independently associated with higher insulin dose/kg (P < 0.001) and holding a below the poverty line certificate (P = 0.004). There was no association between HbA1c and age, sex, caste, religion or experience of stigma. However, the psychosocial burden of T1D (expressed as concern about others learning about the diagnosis, and worry about the future), and experience of stigma were substantial. Percentage of patients with underweight body mass index was significantly lower at the time of study vs. presentation to DT (P = 0.005). Conclusions: The DT charitable programme overcomes social status, gender inequalities and experience of social stigma to provide life-saving treatment to children with T1D in central India. Glycaemic control remains inadequate however, with children living in extreme poverty most at risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".