Unpacking the intractability of childhood stunting: an introduction to the UKRI GCRF Action Against Stunting Hub
Bibliographic record
Abstract
Despite concerted efforts, the global community is off-track in its ambition to reduce the number of stunted children under 5 years by 40% by 2025.1 2 Stunting in children is thought to be a result of adversities in early life with multiple contributors including inadequate nutritional intake, environmental insults and intergenerational transmission of risk. While we know the pathways to stunting are numerous, our understanding of the convergence of these pathways remains a critical block. As such, childhood stunting can be described as a ‘mosaic’ where there is knowledge of the individual components, but inadequate understanding of the interactions or inter-relationships between the individual elements/drivers comprising the whole.3 In addition, the current definition of stunting is based on the measurement of child length, calculated as Length-for-Age-Z score (LAZ) 2 SDs below the median of the WHO growth standard. While this is important in adopting a universally accepted standard definition, the approach is rather too simplistic. The definition does not take into consideration the profoundly complex pathophysiology of stunting and its far-reaching consequences on the growth and development, future health and well-being of the affected children, communities and nations. Accordingly, research on stunting tends to focus on a single outcome or at best, parts of a system, with inadequate holistic exploration of underlying interconnections. Indeed, by defining childhood stunting as a single outcome, it may be argued that we have fostered a lack of understanding of the pathway each child has been on to reach such an outcome. The Action Against Stunting Hub (AASH), a partnership of 18 institutions, funded by the UK Research and Innovation’s Global Challenges Research Fund aims to generate a holistic understanding of the complex drivers of stunting and their interactions to better inform prevention and treatment strategies. This article introduces AASH, the related workstreams and interdisciplinary synergies.
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 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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".