Geographic Targeting of Risk Zones for Childhood Stunting and Related Health Outcomes in Burkina Faso
Bibliographic record
Abstract
Several studies seeking alternative intervention strategies for chronic food insecurity in food-poor nations now advocate the simultaneous evaluation of multiple causative agents to identify and monitor at-risk populations. This study attempted to do so using a three-tiered conceptual framework that expressed childhood nutritional health outcomes as a function of basic, underlying and immediate causes that are manifested at the regional%community level, the household level and the personal level. Focusing on stunting (short stature) as a direct cumulative indicator of food insecurity, the geographic patterns of this nutritional health outcome were mapped using empirical data from Burkina Faso. The spatial analysis revealed several isolated pockets of at-risk populations. Further analysis using logistic regression methods revealed significant disparities in childhood vulnerability based on factors such as urbanization, geographic accessibility, poverty, maternal education and occupation, environmental health, and age, gender and dietary intake of the child. Contrary to research expectations, there were no observed relationships between childhood nutritional health outcomes and the biophysical characteristics of the communities. The odds ratios of stunting in the marginal areas with harsh environmental conditions were comparable to those observed in the wetter, crop-intensive regions. Overall, the findings underscore the need for broadening the scope of research beyond physical environmental conditions to include more socio-economic and anthropogenic factors that result in long-term effects of food insecurity, particularly among young children.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".