Associations of Family Distress, Family Income, and Acculturation on Cognitive Performance using the NIH Toolbox: Implications for Clinical and Research Settings
Bibliographic record
Abstract
Neuropsychologists commonly evaluate raw scores on cognitive tests in relation to a defined reference group to make qualitative interpretations about an individual’s performance. However, these interpretations are only as relevant as the standardized frame of reference used for comparison,which relies on sample size and representativeness. There is growing recognition that the use of conventional norms (e.g., age, sex, years of education, and race) as proxies to capture a broader range of cultural and socioeconomic variability is suboptimal, limiting sample representativeness. The present study evaluated the incremental utility of family income, family conflict, and bidimensional acculturation, above and beyond age, gender, maternal years of education, and race on NIH-Toolbox cognitive performance. A regression-based norming procedure was used as this method may provide more precise estimates of cognitive performance relative to traditional normative tables. Greater family income and lower scores on the Family Environment Scale predicted better performance on the NIH Toolbox subtests, though the effect sizes were very small (r < .05). Scores on the Vancouver Index of Acculturation were not predictive of cognitive performance. Lastly, there were no significant differences between the original NIH Toolbox and new demographically corrected T-scores (Mdiff < 0.50). By traditional statistical standards, the NIH-TB appears to be robust to these sociocultural differences in children between ages 9–10. Practical and clinical contexts in which these small effects may have meaningful impact are discussed.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".