P2‐025: Dementia research with diverse ethno‐cultural populations: Exploring the neuroethics challenges
Bibliographic record
Abstract
More than a half million Canadians over the age of 65 years have dementia and the number is expected to reach 1.3 million in 2034. 25% of Canadians in this age cohort speak neither English nor French as their mother tongue (2006 Statistics Canada Census). Language and ethno-cultural background can affect health care for dementia including care-seeking, access to social support, disease diagnosis and treatment, and response to pharmacotherapy. To ensure the best health outcomes for Canadians, it is necessary incorporate language and culture in research and in the translation of new knowledge to clinical care. We conducted semi-structured, approximately one hour-long interviews with 13 researchers in Canada between June and October 2009. Selection criteria included clinical researchers and social scientists who conduct research on dementia with diverse ethno-cultural populations. The interviews focused on three themes: research design, recruitment and retention, and informed consent. We analyzed interviews using standard methods for content analysis. Different perspectives emerged. Most researchers envisaged potential benefits to culturally and linguistically tailored research protocols. However, they described their resistance to modifying existing protocols given internal barriers such as lack of relevant expertise, and external factors related to the fact that many cognitive measures have not been validated in diverse ethno-cultural populations, and that many tests only exist in English. Other researchers saw no benefit to adapting their protocols, and described diversity as a “nuisance variable,” especially as it relates to recruitment, retention, and consent. This study sheds light on barriers to conducting dementia research with diverse ethno-cultural populations. More research is needed, but our results suggest that fundamental to the problem is a lack of tools and resources for addressing diversity and, in some cases, a sense that, at the present time, the burden is greater than the benefits of doing so. For a country as culturally diverse as Canada, we view this as a significant ethical gap. We conclude that (1) improved resources, (2) interdisciplinary collaboration among dementia researchers and ethicists, and (3) better training curricula that attend specifically to diversity are needed.
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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 | 0.043 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".