EXPLORING RACIAL DIFFERENCES IN WILLINGNESS FOR COGNITIVE TESTING IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Knowledge and attitudes about dementia contribute to diagnosis and treatment outcomes. People of color (POC) are less likely to receive dementia screening and an official dementia diagnosis compared to white adults, leaving them vulnerable to missed diagnosis and early intervention. This study tested whether racial group (0=white, N=255; 1=POC, N=56) moderates associations between knowledge about Alzheimer’s disease (AD) and willingness to undergo cognitive testing. Older adults (N=364, Mage=69.5±6.3 years, 171 women) completed the Perceptions Regarding Investigational Screening for Memory (PRISM; acceptance of AD screening sub-score [PRISM-accept]) and the Alzheimer’s Disease Knowledge Scale (ADKS). After completing the questionnaires, participants were invited to the lab to complete the Montreal Cognitive Assessment (MoCA; 0=not completed, 1=completed). A linear regression determined whether race moderated associations between ADKS and PRISM-accept sub-score, controlling for education and gender. A logistic regression determined whether race moderated associations between ADKS and MoCA completion. Race did not moderate associations between ADKS and PRISM-accept (p>.05). Race moderated associations between ADKS and whether they completed the MoCA (χ2=12.2, p<.001). Specifically, higher knowledge about AD had higher odds of MoCA completion for white older adults (B=.10, p<.001), but lower odds of MoCA completion for POC (B=-.21, p=.03). Results reveal increased knowledge about AD may increase willingness to be screened for white older adults but decrease willingness for POC. Results may be due to factors influencing screening acceptance in POC (e.g., lack of trust). The present study highlights the need to tailor recommendations to increase willingness to be screened for dementia based on race.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".