Associations between early childhood poverty and cognitive functioning throughout childhood and adolescence: A 14-year prospective longitudinal analysis of the Mauritius Child Health Project
Bibliographic record
Abstract
Associations between childhood poverty and cognitive outcomes have been examined from multiple perspectives. However, most evidence is based on cross-sectional data or longitudinal data covering only segments of the developmental process. Moreover, previous longitudinal research has mostly relied on data from Western nations, limiting insights of poverty dynamics in low- and middle-income countries. Here, we use data from the Mauritius Child Health Project, a large-scale prospective longitudinal study conducted in a then low-income country, to examine long-term associations between poverty in early childhood and cognitive performance across childhood and adolescence. Poverty-related factors were assessed at age 3 years and comprised indicators of psychosocial adversity and malnutrition. Cognitive functioning was assessed at ages 3 and 11 years by using standardized intelligence measures and at age 17 years by means of a computerized test battery. Using multiple hierarchical regression models, we found that chronic malnutrition and parental characteristics showed similar-sized, independent associations with initial cognitive functioning at age 3 as well as at age 11 years. For age 17 years, however, associations with early childhood risk factors vanished and instead, cognitive functioning was predicted by performance on prior cognitive assessments. Sex was also found to be a powerful predictor of cognitive trajectories, with boys improving and girls worsening over time, regardless of the level of their initial exposure to risk. The current findings indicate that, to prevent cognitive impairment, interventions tackling poverty and malnutrition should focus on the infancy period and be designed in a gender-sensitive way.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".