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 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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".