A cultural neuropsychological approach to harmonization of cognitive data across culturally and linguistically diverse older adult populations.
Bibliographic record
Abstract
OBJECTIVE: To describe a cultural neuropsychological approach to prestatistical harmonization of cognitive data across the United States (U.S.) and Mexico with the Harmonized Cognitive Assessment Protocol (HCAP). METHOD: We performed a comprehensive review of the administration, scoring, and coding procedures for each cognitive test item administered across the English and Spanish versions of the HCAP in the Health and Retirement Study (HRS) in the U.S. and the Ancillary Study on Cognitive Aging in Mexico (Mex-Cog). For items that were potentially equivalent across studies, we compared each cognitive test item for linguistic and cultural equivalence and classified items as confident or tentative linking items, based on the degree of confidence in their comparability across cohorts and language groups. We evaluated these classifications using differential item functioning techniques. RESULTS: We evaluated 132 test items among 21 cognitive instruments in the HCAP across the HRS and Mex-Cog. We identified 72 confident linking items, 46 tentative linking items, and 14 items that were not comparable across cohorts. Measurement invariance analysis revealed that 64% of the confident linking items and 83% of the tentative linking items showed statistical evidence of measurement differences across cohorts. CONCLUSIONS: Prestatistical harmonization of cognitive data, performed by a multidisciplinary and multilingual team including cultural neuropsychologists, can identify differences in cognitive construct measurement across languages and cultures that may not be identified by statistical procedures alone. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.296 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".