Translation and cultural adaptation of tools to assess diverse Asian American and Asian Canadian subgroups: The Asian Cohort for Alzheimer's Disease (ACAD) Study
Bibliographic record
Abstract
INTRODUCTION: The availability of sociocultural and language-appropriate study materials and instruments is critical for the assessment of cognitive function in people from diverse backgrounds. This report describes the translations and cultural adaptations of study materials for the Asian Cohort for Alzheimer's Disease (ACAD) study. METHODS: We performed translations and cultural adaptations in accordance with the World Health Organization (WHO) translation guidelines to ensure reliable, complete, and culturally appropriate translations from English to the specified Asian languages. RESULTS: We developed Asian language versions of the ACAD documents (consent, data collection packet, and community and social media outreach materials) reflecting the sociocultural backgrounds of the ACAD target population (i.e., older Asian adults) DISCUSSION: The multistep translation process accounting for distinctive Asian sociocultural and language backgrounds provides an important guideline for Alzheimer's disease and related dementias (ADRD) researchers to promote health literacy and research with underrepresented Asian American and Canadian adults. HIGHLIGHTS: Asian American and Asian Canadian older adults are the fastest-growing populations. A lack of linguistically and culturally appropriate cognitive assessment tools creates barriers for quality healthcare and clinical research. We report the translations and cultural adaptations of the Asian Cohort for Alzheimer's Disease (ACAD) study materials into Chinese, Korean, and Vietnamese. This translation methodology should be extended to Asian Indians, Filipinos, and other Asian American or Asian Canadian populations.
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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 | 0.040 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".