Cognitive Neuroimaging Studies on Poverty and Socioeconomic Status Differences in Children and Families across the World: Translational Insights for Next Decade’s Policy, Health, and Education
Bibliographic record
Abstract
This systematic review and meta-analysis of global peer-reviewed neuroimaging findings preliminarily assessed the magnitude of effect sizes (ES) of the influences of family poverty/low socioeconomic status (SES) on children’s neurocognition and whether these were consistently detrimental. The literature search (Web of Science; PUBMED; MEDLINE: PSYCNET; GOOGLE SCHOLAR; SCIENCEDIRECT) included 66 studies from 1988 to 2022; 85% of the studies included were conducted in Western, high-income nations. Bayesian models, corrected by study sizes and variances, revealed ESs were heterogeneous across countries and measurements. Bayesian and standard hypothesis testing indicated high and low SES groups showed similar behavioral performances in neuroimaging-concurrent tasks. Except for Magnetic Resonance Imaging studies, ESs were small-to-intermediate with modest reliability. The strongest ESs were found for attention, mathematical performance, language, and cortical volume, followed by intermediate ESs for reading and socioemotional processes. Differentials in resting activity and connectivity, working memory, and executive functions yielded small effects. A bibliometric analysis showed a significant proportion of the literature attributed neurocognitive deficits to low SES, despite overlooking the under-representativity of non-Western and low-income countries, potential influences of racial/ethnic differences, and measurement sensitivity/specificity discrepancies. To reach United Nations Sustainable Development Goals, policies and interventions should consider regional, structural, or environmental ecologies beyond the individual, critically probing implicit deficit attributions.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".