Successful delivery of nutrition programs and the sustainable development goals
Bibliographic record
Abstract
Malnutrition affects millions of people globally, especially women, children, and other vulnerable populations. Sustainable Development Goals (SDGs) were set in 2015 to end poverty, protect the planet, and improve the lives and prospects of everyone by 2030. To achieve the SDG goals effective nutrition interventions and programs need to be efficiently delivered to those most in need. Nutrition directly affects 2 SDGs (2 and 3) and indirectly influences five others. In addition, almost all SDGs influence nutrition and thus attaining the SDG goals is also a pre-requisite to achieving the Global Nutrition targets set in 2012. Evidence-based nutrition interventions, for which there is strong evidence of their biological impact, have the potential to directly influence SDGs 2 and 3 if successfully delivered at scale in high-burden countries. Nevertheless, delivery of nutrition programs is a complex process, where policy, government commitment, adequate budget allocation, supplies and delivery systems, training of service providers, informed beneficiaries and program monitoring and evaluation all need to be in place and aligned with each other. Although in the past decade there has been progress in the SDGs that nutrition directly affects, many goals are still off-track, likely due to several pending gaps at policy-level, program-level, and intervention-level. To accelerate the progress toward reaching the SDG goals that are directly influenced by nutrition, countries need to be supported to successfully and sustainably deliver proven interventions and to scale-up and deliver new interventions in new and innovative ways, and the evidence base should be built in promising areas especially integrating (rather than prioritizing over each other) nutrition-specific and sensitive approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".