Distinct clusters of stunted children in India: An observational study
Bibliographic record
Abstract
Childhood stunting is often conceptualised as a singular concept (i.e., stunted or not), and such an approach implies similarity in the experiences of children who are stunted. Furthermore, risk factors for stunting are often treated in isolation, and limited research has examined how multiple risk factors interact together. Our aim was to examine whether there are subgroups among stunted children, and if parental characteristics influence the likelihood of these subgroups among children. Children who were stunted were identified from the 2005-2006 Indian National Family Health Survey (n = 12,417). Latent class analysis was used to explore the existence of subgroups among stunted children by their social, demographic, and health characteristics. We examined whether parental characteristics predicted the likelihood of a child belonging to each latent class using a multinomial logit regression model. We found there to be 5 distinct groups of stunted children; "poor, older, and poor health-related outcomes," "poor, young, and poorest health-related outcomes," "poor with mixed health-related outcomes," "wealthy and good health-related outcomes," and "typical traits." Both mother and father's educational attainment, body mass index, and height were important predictors of class membership. Our findings demonstrate evidence that there is heterogeneity of the risk factors and behaviours among children who are stunted. It suggests that stunting is not a singular concept; rather, there are multiple experiences represented by our "types" of stunting. Adopting a multidimensional approach to conceptualising stunting may be important for improving the design and targeting of interventions for managing stunting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".