Cognitive screening among older afro‐caribbean adults in rural areas of South Florida
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease and related dementias (ADRD) disproportionately affects communities of color with older black Americans performing worse than Whites on cognitive tests and facing higher ADRD risks.1,2 Contributors to low diagnostic rates include inadequate knowledge, cultural norms, low health literacy, and an unwillingness to discuss early signs of disease. 1,3‐5 Older Afro‐Caribbeans are a growing subset of the population, particularly in rural South Florida, but best practices to screen for ADRD are unknown.6 Many instruments, including the Montreal Cognitive Assessment (MoCA) have not been assessed within this population. We report on the use of the MoCA 5‐minute test/telephone (Mini‐MoCA) as a screening tool in older, rural‐dwelling Afro‐Caribbeans.7 Method We conducted survey interviews with 53 Afro‐Caribbean participants. Participants were assessed with the 5‐minute Mini‐MoCA, which included tests of language fluency, orientation and recall. Linear regression models were used to investigate the association between different components of the Mini‐MoCA its effects on age, sex, and education. Result A total of 53 Afro‐Caribbean participants (67.2 +10.8y (Mean ± SD), 68% with 10y or less of education) residing in rural South Florida within the last 20 years were included. Participants were largely from Haiti (66%) with 34% from other Caribbean countries including Cuba, Jamaica, Puerto Rico, and Barbados. About 80% of participants reported not having their memory tested prior to the study. The Mini‐MoCA demonstrated good reliability in this sample (α = 0.734). We found that 53% had a total score of 11 or lower on the Mini‐MoCA. Linear regression suggested a significant association between Mini‐MoCA score and education (p = .001). Conclusion While the Mini‐MoCA showed good reliability in low‐educated older Afro‐Caribbeans, scores were strongly dependent on years of education. Disparities in educational attainment may contribute to disparities in performance and affect true detection on cognitive impairment in these communities. Social and cultural factors must be considered when interpreting the Mini‐MoCA, given the high error rates on items that depend on the ability to read and write. Study results highlight the need to use a language‐neutral test to accurately measures cognitive impairment among this specific population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".