Caregiver Experiences Across Three Neurodegenerative Diseases: Alzheimer’s, Parkinson’s, and Parkinson’s With Dementia
Bibliographic record
Abstract
OBJECTIVE: This article asks whether distinct caregiver experiences of Alzheimer's disease (AD), Parkinson's disease (PD), and Parkinson's disease with dementia (PDD) spouses are accounted for by disease diagnosis or by a unique combination of symptoms, demands, support, and quality of life (QOL) cross disease groups. METHOD: One hundred five live-in spouse caregivers (71.4 ± 7 years) were surveyed for persons with AD (39%), PD (41%), and PDD (20%). A hierarchical cluster analysis organized caregivers across disease diagnosis into clusters with similar symptom presentation, care demands, support, and QoL. RESULTS: Four clusters cut across disease diagnosis. "Succeeding" cared for mild symptoms and had emotional support. "Coping" managed moderate stressors and utilized formal supports. "Getting by with support" and "Struggling" had the greatest stressors; available emotional support influenced whether burden/depression was moderate or severe. The results remain the same when diagnostic category is added to the cluster analysis. DISCUSSION: This study supports going beyond disease diagnosis when examining caregiver experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".