MEASURING COGNITION IN NSHAP USING MULTIMODE DATA COLLECTION
Bibliographic record
Abstract
Abstract The National Social Life, Health, and Aging Project (NSHAP) is a U.S. nationally representative, omnibus study of social relationships and health among home-dwelling older adults. Cognitive function is a key focus, both as an outcome and also because it is implicated in mechanisms by which social relationships affect and are affected by health trajectories. Data collection started in 2005, and respondents have been re-interviewed in 2010, 2015, and 2022–3. Starting in 2010, NSHAP administers a survey-adapted version of the Montreal Cognitive Assessment (MoCA-SA). While Rounds 1–3 were conducted in the home, Round 4 utilized three remote modes (web, phone and PAPI, in that order) in addition to in-home interviews. Respondents were assigned to remote data collection initially if they were judged to be likely to respond based on prior information, otherwise they were assigned to in-home; a random subset of 400 initially assigned to remote were shifted to the in-home group to facilitate examination of possible mode effects on response. Each mode necessarily utilized a slightly different subset of the MoCA-SA items based on feasibility; these were augmented by items from the Rush Alzheimer’s Disease Center’s (RADC) cognition battery that could be administered via the web or PAPI. This presentation will describe our analytic strategy for harmonizing across modes and generating an overall measure of cognitive function, as well as preliminary results for Round 4. Our approach utilizes a bi-factor item response model calibrated to each mode, and is applicable to data from other studies.
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 | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".