India’s NCD strategy in the SDG era: are there early signs of a paradigm shift?
Bibliographic record
Abstract
BACKGROUND: The Sustainable Development Goals (SDGs) are seen in most corners as the embodiment of a more inclusive and holistic development approach, key to addressing the numerous and urgent challenges the world faces. In the health realm, a true SDG approach will require a five-fold paradigm shift according to Buse and Hawkes. This article explores whether early traces of this paradigm shift can already be witnessed in the Indian context, focusing on Non-Communicable Diseases (NCDs) more in particular. DISCUSSION: By now, NCDs make up a large health burden in India, both individually and on the health system. Inspired by an SDG vision, tackling NCDs will require a comprehensive approach rooted in preventive, curative and rehabilitative services. In India, some early momentum in this respect can already be witnessed, certainly in addressing the first two challenges identified by Buse and Hawkes, leadership and intersectoral coherence, and a shift from treatment to prevention. A central plan addressing health through an inter-sectoral approach has shaped the trajectory so far, moving away from silos to engagement with sectors beyond health. New guidelines addressing comprehensive primary healthcare propose a community outreach and preventive approach for NCDs. At a broader level, NCD prevention is also closely linked to tackling the so called "commercial determinants of health" and will require among others strong (central and state level) regulation, teaming up with global advocacy networks and capitalizing on global frameworks, where they exist. Strong political leadership will be indispensable for this, and is according to Buse and Hawkes closely linked to seeing health as a right and the government as accountable when it comes to providing for the right to health through its policies and actions. CONCLUSION: National stewardship will thus be key, via a more adaptive network governance structure with the central level coordinating with the state level to ensure implementation, while also engaging with other stakeholders, sectors, the private sector and civil society. As one can expect, networked governance, necessary for the battle against NCDs, is a work in progress in India. In sum, some of the early (paradigm shift) signs are encouraging, but by and large it is still too early to assess whether a real paradigm shift has taken place.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".