Ethnic disparities in care needs among individuals with cognitive impairment
Bibliographic record
Abstract
BackgroundAs more individuals with cognitive impairment and dementia (CID) remain at home, greater needs arise, necessitating additional support.ObjectiveTo examine ethnic differences in the needs of individuals with CID among Mexican American (MA) and non-Hispanic White (NHW) participants.MethodsAdults 65 + with possible cognitive impairment (Montreal Cognitive Assessment score < 26), and their caregivers living in Nueces County, Texas, were included. We used the Camberwell Assessment of Need for the Elderly (CANE) tool to study the needs (accommodations, self-care, continence, physical health, emotional well-being, social relationships, and availability of support networks) and their domains of individuals with CID including environmental, physical, psychological and social needs. Using negative binomial and Poisson regressions, ethnic differences were examined within each domain.ResultsA total of 473 participants were included. NHW participants (N = 150) were slightly older (75.5 versus 72.7 years) and had higher rates of MCI and dementia (55% versus 47%) compared to MA participants (N = 323). All participants reported high levels of needs (met or unmet). Furthermore, although NHW participants reported having fewer social needs (met or unmet) compared to MA participants (Incident Rate Ratio [IRR]=-0.79; 97.5%CI:0.63-0.98), NHW participants had a greater level of unmet needs when it came to social needs compared to MA participants (IRR = 1.85; 97.5%CI:1.33-2.57).ConclusionsFindings indicate high levels of needs among individuals with CID. There also exist ethnic differences, with NHW participants having greater unmet needs in social areas. Enhancing access to resources and support systems is essential for equitable support for individuals with CID across various ethnic backgrounds.
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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 | 0.002 |
| 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 | 0.001 |
| 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".