Too much information? Asian Americans’ preferences for incidental brain MRI findings
Bibliographic record
Abstract
INTRODUCTION: South Asian (SA) and East Asian (EA) older adults represent the fastest growing group of Americans at risk for dementia, but their participation in aging and dementia research has been limited. While recruiting healthy SA older adults into a brain health study, we encountered unexpected hesitancy towards structural brain MRI analysis along with some stigmatizing attitudes related to internal locus of control (LoC) for future dementia risks. We hypothesized that support for MRI-related research was influenced by these attitudes as well as one's own MRI experience, perceived MRI safety, and concerns for one's own risks for future dementia/stroke. METHODS: We developed a brief cross-sectional survey to assess older adults' MRI experiences and perceptions, desire to learn of six incidental findings of increasing health implications, and attitudes related to dementia as well as research participation. We recruited a convenience sample of 256 respondents (74% reporting as 50+) from the New Jersey/New York City area to complete the survey, and modeled the proportional odds (P.O.) for pro-research attitudes. RESULTS: 77 SA and 84 EA respondents were analyzed with 95 non-Asian adults. White (P.O.=2.54, p=0.013) and EA (P.O.=2.14, p=0.019) respondents were both more likely than SA respondents to endorse healthy volunteers' participation in research, and the difference between White and SA respondents was mediated by the latter's greater internal LoC for dementia risks. EA respondents had more worries for future dementia/stroke than SA respondents (p=0.006), but still shared SA respondents' low desire to learn of incidental MRI findings. DISCUSSION: SA and EA older adults had different attitudes towards future dementia/stroke risks, but shared a low desire to learn of incidental MRI findings. A culturally-appropriate protocol to disclose incidental MRI findings may improve SA and EA participation in brain health research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".