Climate Care India: A protocol for digital transformation of health systems for non-communicable disease management and climate change adaptation
Bibliographic record
Abstract
Abstract Background and objective Non-communicable diseases (NCDs) account for 74% of global deaths, disproportionately affecting low-resource settings in the global south. The increasing frequency and severity of climate change-related events worsen the NCD burden – particularly in low-resource settings – thereby necessitating health system transformation. This longitudinal trial aims to transform the current response to climate change and NCDs among affected communities via a customized digital health platform. Methodology Building on the intergenerational Youth Adolescents’ behaViour, musculoskeletAl heAlth, Growth & Nutrition (YUVAAN) prospective cohort study – which has enrolled 1070 rural households as of November 2024 in Western India – a digital platform will be tailored to monitor and address evolving climate change and NCD-related risks. The three-phase methodology includes: 1) adaptation: co-developing the platform with a Citizen Scientist Advisory Council for local use, integrating culturally relevant features and languages; 2) implementation: pilot testing the platform and step-wedge deployment within the YUVAAN cohort; 3) evaluation: conducting mixed-method analyses of the platform, climate change, and health outcome associations. A sample size of 978 families was calculated to detect an effect size of 0.3 (90% power, 0.05 α, 15% attrition). Discussion Through access to real-time data, the digital platform will provide rural households with personalized support for NCD prevention and management, while enabling climate change preparedness and adaptation strategies in participating communities. Conclusion Integrating digital platforms into local decision-making will strengthen health systems’ capacity to manage NCDs in rural and low-resource settings impacted by climate change. These platforms can enable real-time data access for personalized care and evidence-based decision-making.
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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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.160 | 0.043 |
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".