Learning From Recruitment Challenges: Barriers to Diagnosis, Treatment, and Research Participation for Latinos With Symptoms of Alzheimer's Disease
Bibliographic record
Abstract
This article discusses barriers to diagnosis and treatment of Alzheimer's disease (AD) and concomitantly to participation in AD research as elicited from 29 potential Latino participants who ultimately did not enroll in a study evaluating a caregiver intervention. Nearly half of all individuals contacting the researcher about the intervention study failed to meet criteria stipulating an existing AD diagnosis. Barriers to obtaining a diagnosis include lack of knowledge about AD, perceptions of memory loss as normal aging, and structural barriers to accessing care. A quarter of caregivers contacting the researcher felt too overwhelmed to participate. Many of these barriers have been previously identified as challenges to treatment, suggesting this is not just a methodological research problem, but inextricably tied to larger issues of AD knowledge and service accessibility. Engaging Latino communities equitably in the assessment of needs and the process of addressing them, thus ensuring the validity and applicability of the research and findings, is important both for increasing this group's participation in relevant studies and for addressing existing health disparities.
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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 | 0.084 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".