COGNITION MEASURES AND FINDINGS IN THE NATIONAL SOCIAL LIFE, HEALTH, AND AGING PROJECT
Bibliographic record
Abstract
Abstract Good cognitive function is an important component of health at any age. Certain domains of cognition tend to decline with age and rates of change vary dramatically across individuals and across social groups. This Symposium examines a commonly-used clinical measure of cognition, the Montreal Cognitive Assessment, adapted for survey use (MoCA-SA) and administered in two rounds of the National Social Life, Health and Aging Project (2010 and 2015). It focuses especially identifying and evaluating differential functioning of the MoCA-SA across racial and ethnic groups, across modes of administration of the measure, within intimate dyads and as linked to sensory function. Iveniuk and colleagues examine race difference and find that 7 measures, out of the 18 used in NSHAP’s MoCA, formed a scale that was more robust to racial bias and suggest use of this modified measure to compare racial groups. Piedra and coauthors construct an abbreviated MoCA-SA (Spanish version) that compared favorably with the long form MoCA across the different grouping and showed predicative validity with consequential outcomes associated with cognitive decline. Pudelek and colleagues examine mode of assessment. web, phone, and PAPI or in-person interviews and describe an analytic strategy for obtaining a measure comparable across modes. Meiyi Li and Yiang Li find that women’s cognitive impairment adversely affects their partner’s social connectedness but husband’s impairment does not. Zhong et al find that odor identification was associated with some domains of cognition with differences in that association by age, gender, or race, but not by education.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".