Quality in Addition to Quantity of Education Impacts Performance on Cognitive Screening (P4.004)
Bibliographic record
Abstract
Objective:To determine the independent association of quality of education on cognition, and understand its effect on racial differences in cognition. Background:Screening tests of global cognition often detect racial differences in scores even after accounting for educational attainment. However, differences in quality of education may independently contribute to cognitive performance, and explain residual group differences in cognition. Methods:Community dwelling adults over the age of 55 were drawn from an ongoing longitudinal study of aging. Quantity of education was defined as the total number of years of education completed. Quality of education measures included three high school characteristics: 1) average number of academic courses offered; 2) school type; and 3) school district/region. Cognition was assessed using the Montreal Cognitive Assessment (MoCA). We performed three linear regression models with MoCA score as the dependent variable and each quality of education measure as the principal independent variable. Results:A total of 512 subjects (72.9[percnt] African-American, 27.1[percnt] White; mean age 65 years) who attended high school in Philadelphia between 1968 and 1975 were included. A significant correlation between MoCA scores and average number of academic courses offered was observed (r=0.23; p<0.05). Individuals who went to parochial school had higher MoCA scores than those who went to public and vocational school (26 vs 25 vs 24, p<0.001). In the multivariate analysis, the effect of average number of academic courses offered and school type remained significant when education level and age was added (p=<0.05); however, race was no longer significant. Lastly, the effect of MoCA scores by school district/region remained significant when education level was added (p<0.001), but not for age and race. Conclusions:More detailed information about educational attainment beyond years of education may help explain racial differences in cognitive scores. Study Supported By:Parkinson Council, NIA(K23 AG034236 and P3OAG031043) and Penn Minority Aging Research for Community Health(MARCH)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".