Using a multi‐staged translation method to develop socio‐culturally and language‐sensitive study materials: Lessons learned from an Asian cohort for Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a global health crisis on multiple levels. A major impediment to AD research is the availability of socio‐culturally and language appropriate study materials and instruments and inclusion of under‐represented minorities. To fulfill this need, we apply best practices and lessons learned to translate study materials for use in the Asian Cohort for Alzheimer’s Disease’s (ACAD) study of AD in Asian Americans and Asian Canadians (ASAC) with the overarching goal in inclusivity of these populations to better understand the etiology of AD. Method Our multi‐stag translation process is guided by the World Health Organization’s (WHO) process of translation (forward and reverse translation, consensus verification and reconciliation) to achieve parity with the English materials for our target groups (Chinese (simplified and traditional; Mandarin and Cantonese), Korean and Vietnamese). ACAD’s collective experience partnering with aging Asian adults has revealed not only a language barrier, but also issues with socio‐cultural communication traits, and literal translations that stigmatize AD and dementia. Consequently, we have taken into account the varying perceptions and expressions of words in a social context. The translation process was implemented by a team of multi‐lingual researchers/clinicians/community leaders with extensive practical translation experience. Since language influences how a speaker views the world, we employed a cross‐section of translators and emphasized conceptual (vs. literal) culturally appropriate translations for the community. Results To date, we have developed Asian language versions of ACAD documents (informed consent, data collection, community and social media outreach materials, website) for all our target groups and continue the process of beta‐testing our study materials (including the cognitive assessment instruments). Conclusion The multi‐stage translation process accounts for distinctive Asian socio‐cultural‐language backgrounds, providing an important guideline for AD researchers to promote health literacy in the health and general community in an effort to reduce health disparities in underrepresented groups, like Asians. In order to ensure fidelity across languages, we will continue validating the original English version in Chinese, Korean, and Vietnamese versions in the ACAD study.
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 | 0.164 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".