Low Health Literacy: An Under-Recognized Obstacle in Parkinson’s Disease (P3.075)
Bibliographic record
Abstract
OBJECTIVE: To define the prevalence and predictors of low health literacy (HL) in community-dwelling patients with Parkinson’s Disease (PD) at an academic referral center, and to explore potential associations with adverse health outcomes. BACKGROUND: Low HL indicates a limited ability to understand and use basic information to make appropriate healthcare decisions. Low HL is associated with poorer health outcomes in multiple conditions, but has yet to be examined in the PD population. DESIGN/METHODS: Cross-sectional study of adults with PD participating in the National Parkinson Foundation registry at the University of Pennsylvania. Subjects were administered two brief assessments of health literacy_the Rapid Estimate of Adult Literacy in Medicine-Short Form (REALM-SF), a simple word-recognition test, and the Newest Vital Sign (NVS), a test of basic literacy, numeracy and understanding of health information_in demographic, clinical, and resource utilization questionnaires. RESULTS: 121 subjects completed both HL screens (median age 67 years, 67.8% male, 95.9% Caucasian, median 16 years of education). Median PD duration was 16 years and the majority were stage Hoehn & Yahr II (61.9%). Median Montreal Cognitive Assessment (MoCA) score was 26 (IQR 23-29). Using the REALM-SF, 91.7% of subjects had adequate literacy. With the NVS, however, 45.5% of our sample had low HL, which was significantly associated with older age, lower education, advanced PD stages, and poorer cognition. Finally, the odds of hospitalization was 3.2 times higher in individuals with low HL after adjusting for age, PD stage, and cognition (p = 0.05). CONCLUSIONS: Low HL is common in our highly-educated, community-dwelling sample of PD patients and is a risk factor for hospitalization after adjusting for other potential confounders. Thus, recognizing and addressing low HL in patient care settings is essential. Prospective studies should explore the relationship between low HL and adverse outcomes to optimize interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".