Measuring Cognitive Function In-Person and Remotely in Round 4 of the National Social Life, Health, and Aging Project
Bibliographic record
Abstract
OBJECTIVES: This paper describes the changes made to the collection of cognitive measures when the National Social Life, Health, and Aging Project (NSHAP) introduced remote modes of data collection. METHODS: In Round 4 (2021-2023), the longitudinal study transitioned from being conducted in-person to collecting data via multiple modes including in-person and remote modes: web, phone, and paper-and-pencil. The team began with the measures used in Rounds 2 and 3 of NSHAP-the survey-adapted Montreal Cognitive Assessment (MoCA-SA)-and evaluated which measures could be administered remotely, introducing new measures for each cognitive subdomain, as needed, to compensate for items that could not be administered remotely. RESULTS: Cognitive items used in Rounds 2 and 3 that could not be administered remotely were dropped from the respective modes, and items selected from the Rush Alzheimer's Disease Center's (RADC) global cognition battery were added as substitutes. For comparison, the RADC substitute items were added to the in-person mode making it longer in Round 4. DISCUSSION: The changes in cognitive measures resulted in different numbers of cognitive items across the 4 modes of survey administration in Round 4. Analysts should be aware of these changes when creating a single global cognition score for the entire NSHAP sample in Round 4, and aware that there may be mode effects that could affect cognition scores.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".