Caregiving concerns and clinical characteristics across neurodegenerative and cerebrovascular disorders in the Ontario Neurodegenerative Disease Research Initiative
Bibliographic record
Abstract
Objectives: In the Ontario Neurodegenerative Disease Research Initiative (ONDRI), we aimed to ask and answer: (1) How many and what types of burdens are captured by the Zarit’s Burden Interview (ZBI)? (2) Do we see categorical or spectrum-like effects for burden(s)? and (3) Which if any demographic, clinical, and cognitive measures are related to burden(s)?Methods: N = 504 participants and their study partners (e.g., family, friends) across: Alzheimer’s disease/mild cognitive impairment (AD/MCI; n = 120), Parkinson’s disease (PD; n = 136), amyotrophic lateral sclerosis (ALS; n = 38), frontotemporal dementia (FTD; n = 53), and cerebrovascular disease (CVD; n = 157). Study partners provided information about themselves, and information about the clinical participants (e.g., activities of daily living). We used Correspondence Analysis to identify types of caregiving concerns in the ZBI, then identified relationships between those concerns and demographic and clinical measures, and a cognitive battery.Results: We found three components in the ZBI. The first was “overall burden” and was (1) strongly related to increased neuropsychiatric symptoms and decreased independence in activities of daily living, (2) moderately related to cognition, and (3) showed little-to-no differences between disorders. The second and third components showed four types of caregiving concerns: current care of patient, future care of patient, personal concerns of study partner, and social concerns of study partner. Discussion: Caregiving concerns are individual experiences and emphasize the importance of support for management of activities of daily living and neuropsychiatric symptoms, and underscore individualized needs for caregiving assessment and education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".