Measuring Cognition: The Chicago Cognitive Function Measure in the National Social Life, Health and Aging Project, Wave 2
Bibliographic record
Abstract
OBJECTIVES: To describe the development of a multidimensional test of cognition for the National Social life, Health and Aging Project (NSHAP), the Chicago Cognitive Function Measure (CCFM). METHOD: CCFM development included 3 steps: (a) A pilot test of the Montreal Cognitive Assessment (MoCA) to create a standard protocol, choose specific items, reorder items, and improve clarity; (b) integration into a CAPI-based format; and (c) evaluation of the performance of the CCFM in the field. The CCFM was subsequently incorporated into NSHAP, Wave 2 (n = 3,377). RESULTS: The pre-test (n = 120) mean age was 71.35 (SD 8.40); 53% were female, 69% white, and 70% with college or greater education. The MoCA took an average of 15.6min; the time for the CCFM was 12.0 min. CCFM scores (0-20) can be used as a continuous outcome or to adjust for cognition in a multivariable analysis. CCFM scores were highly correlated with MoCA scores (r = .973). Modeling projects MoCA scores from CCFM scores using the equation: MoCA = (1.14 × CCFM) + 6.83. In Wave 2, the overall weighted mean CCFM score was 13.9 (SE 0.13). DISCUSSION: A survey-based adaptation of the MoCA was successfully integrated into a nationally representative sample of older adults, NSHAP Wave 2.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".