Translation and Cultural Adaptation of Tools to Assess Diverse Asian American and Asian Canadian Populations: The Asian Cohort for Alzheimer’s Disease Study
Bibliographic record
Abstract
Abstract Background Socio‐cultural and language‐appropriate study materials and instruments are critical for accurate assessment of cognitive function in people from diverse backgrounds. Most research uses cognitive tests based on Western, industrialized, English‐speaking cultures and may not reflect global experiences. The purpose of this study was to describe the translations of study materials and cultural adaptations that were developed for the Asian Cohort for Alzheimer’s Disease (ACAD). Methods Multi‐lingual researchers, clinicians, and community leaders with extensive practical translation experience created materials and instruments in accordance with World Health Organization (WHO) guidelines, using a translation process that consisted of preparing materials, translating materials, conducting a committee review, conducting a pretest (as appropriate), and conducting necessary revisions. The ACAD study population speaks Chinese (Simplified and Traditional), Korean, or Vietnamese. The majority of instruments in the protocol harmonize with the National Alzheimer’s Coordinating Center purposefully, warranting both literal and conceptually cultural‐appropriate translations for our older Asian American and Asian Canadian participants. Results We have developed Asian language versions of the ACAD informed consent, data collection packet, and community outreach materials for all our target groups. Many cognitive tools had been previously translated into Asian languages and made minor modifications. Some items required cultural adaptations to reflect the socio‐cultural background of the targeted Asian languages, e.g., the types of physical activities or dietary choices surveyed. Conclusion This multi‐stage translation process accounts for the distinctive socio‐cultural and language backgrounds of each Asian population. Alzheimer’s disease and related dementias researchers interested in engaging with these populations may apply this translation process to reduce health disparities in these underrepresented populations. To ensure fidelity across different languages, ACAD will continue to engage in this comprehensive translation process with our diverse community stakeholders.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".