A theory of cultural translation in healthcare for multilingual older adults living with dementia and their caregivers
Bibliographic record
Abstract
Objective: The United States is a multicultural, multilingual country; as a recognized feature of the American population, the challenges of caregivers seeking services for multilingual older adults still need to be better understood. This study aims to understand the experience of caregivers who sought healthcare services for a multilingual older adult living with dementia.Methods: Using Constructivist Grounded Theory, formal and informal caregivers participated in semi-structured interviews to ascertain their experiences seeking health services for multilingual older adults with dementia.Results: Several themes emerged, including Cultural Translator, Mitigating Relationships, Leaning, Seeking Help, Meeting them where they are, and a Rigid Healthcare System. Lastly, the participants’ descriptions unveiled a phenomenon identified as cultural translator stress. Cultural Translator stress may occur due to the added responsibilities of advocacy, healthcare system navigation, language interpretation, and explanations of culturally based idioms on behalf of the multilingual older adult with dementia for the healthcare provider.Conclusions: As our understanding of care for multilingual older adults with dementia improves, awareness of their caregiver's needs and mechanisms to support this unique population should emerge. Factors such as culture, access to culturally appropriate services, and services needed to support family caregivers are needed. Further studies are needed to understand the stressors related to caring for a multilingual adult living with dementia or the phenomenon of cultural translator stress.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".