Stunting and academic trajectory in urban settings of Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Impaired growth in childhood can lead to poor cognitive development and low school performance. However, literature on the effects of stunting on school trajectory is very limited. The primary objective of this research was to estimate the age at which children start school according to levels of height-for-age z-score (stunting). A second objective was to estimate the gain in terms of age at school entry associated with an improvement in height-for-age z-score. A third objective was to explore the relationship between stunting, grade repetition, and school dropout. METHODS: We used longitudinal data from the Ouagadougou (Burkina Faso) Health and Demographic Surveillance System. Data from a 2010 health survey of children under 5 years of age were merged with subsequent longitudinal schooling data. The study included 767 children globally who participated in the health and education surveys. Education data allowed us to apprehend academic trajectory measured by age at school entry, repetition, and dropout. The health survey gathered anthropometric information that was used to measure stunting. The adjusted age at school entry was estimated using a Poisson model. The gain represents the difference in adjusted age at school entry for different values of height-for-age. The relationship between stunting and grade repetition and dropout was studied using a discrete-time survival model. RESULTS: Results showed that children entered school on average at 5.7 years old, and the incidence of grade repetition and dropout was 17.7 and 6.6 per 100 persons-years, respectively. The adjusted age at school entry of severely stunted children was 6.2 years [95% confidence intervals (CI): 6.1; 6.3] compared to 5.1 years [95% CI: 5.0; 5.3] for children who had normal growth. The difference (gain) in adjusted age at school entry between severely and non-stunted children was thus 1.06 [95% CI: 0.87; 1.25] years. If a child's growth changed from severe stunting to normal growth, their risk of repeating a grade decreased by 5.0 [95% CI: 0.0; 9.0] per 100 persons-years. We did not observe a relationship between height-for-age and dropout. CONCLUSION: The results show that schooling is affected in several ways for children who are stunted. The age at school entry of stunted children is more likely to be delayed. Also, being stunted is associated with higher incidence of grade repetition. However, the relationship between stunting and dropout was inconclusive.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".