Stunting, age at school entry and academic performance in developing countries: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To review evidence of the effects of stunting, or height-for-age, on schooling level and schooling trajectories, defined as the combination of school entry age, grade repetition and dropouts. METHODS: We conducted a systematic review of studies (last update 20 March 2021) that assessed the association between stunting, or height-for-age, and at least one component of school trajectory using five databases (PubMed, Embase, Education Resources Information Center [ERIC], Web of Science and PsycINFO). Two independent reviewers performed study selection and data extraction. Pooled effects were calculated using the generic inverse variance weighting random-effect model. The risk of bias was assessed using the ROBINS-I tool (PROSPERO ID: CRD42020198346). RESULTS: We screened 3944 articles, and 16 were eligible for the qualitative and quantitative syntheses. Meta-analysis showed that an increase in height-for-age leads to an increase in early enrolment [OR = 1.34 (95% CI, 1.07-1.67)], a reduction in late enrolment [OR = 0.63 (95% CI, 0.51-0.78)], an increase in schooling level [MD = 0.24 (95% CI, 0.14-0.34)] and a reduction in school overage [OR = 0.79 (95% CI, 0.70-0.90)]. Stunted children were more likely to repeat a grade than non-stunted [OR = 1.59 (95% CI, 1.18-2.14)]. CONCLUSION: This review suggests that stunting in childhood might negatively affect school trajectories. Future research should evaluate the effect of stunting on school trajectories and the modification effect of socioeconomic status.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".