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
The purpose of this global systematic review and meta-analysis aimed to preliminarily assess: 1) whether the effects of family poverty or low SES on neurocognition as reflected by neuroimaging results are consistently found to be detrimental; 2) the strength of the effect sizes (ES). The preliminary literature search on main databases (WEB of SCIENCE; PUBMED; MEDLINE: PSYCNET; GOOGLE SCHOLAR; SCIENCEDIRECT) included 66 experimental studies and intervention studies from 1988 to 2022. Only ten (15%) were from middle or developing countries; most of the studies (85%) were conducted in Western countries. Least squares Bayesian ANOVA models, weighted by sample sizes, revealed very strong evidence that the estimated ESs were statistically heterogenous across countries. A mix of Bayesian, standard hypothesis testing (parametric and non-parametric) sets of analyses all converged to indicate unequivocally that, despite the heterogeneity across studies, high and low SES groups performed similarly in most of the behavioral tasks concurrent with neuroimaging. Except for combined fMRI+sMRI studies, which yielded very large effect sizes, the effects were generally small to intermediate with rather modest reliability in the findings. The strongest effect sizes for differences between high and low SES were found in relation to mathematical performance, language and socioemotional processes, closely followed by intermediate effects concerning attention and working memory. Differentials in resting networks, reading and executive functions generally yielded small effects. Finally, a bibliometric analysis of the surveyed literature shows that the most common interpretation of the difference between high vs low SES children is a presumed implicit brain or cognitive deficit in the latter group. We suggest these results are best understood in relation to global structural or environmental set of ecological factors beyond the individual children and families. Policies and interventions for health and education which target poor and low SES children and families which implicitly attribute an individual-level neurocognitive deficit to disadvantaged groups risk using a one-size-fits-all approach which exacerbates the issues linked with social inequality. To reach United Nations Sustainable Development Goals, we argue that there needs to be an epistemological global evidence-based transition to combat the status quo of privilege and deficit thinking.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.002 | 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".