Caring for Stroke Survivors: Baseline and 1-Year Determinants of Caregiver Burden
Bibliographic record
Abstract
BACKGROUND: Caregiver burden following stroke can have significant adverse health consequences for caregivers and threatens the recovery and successful rehabilitation of patients. Our objective was to identify patient factors that contribute to higher levels of caregiver burden. METHODS: We prospectively studied patients admitted to our stroke unit over a 2-year period (2001-2002). Data were collected at baseline and at 1 year. Caregiver burden was measured at 1 year using the Relatives Stress Scale (completed by 155 caregivers) and the Bakas Caregiver Outcomes Scale (143 caregivers). Explanatory patient factors at baseline included sociodemographic status, stroke severity, stroke sub-type, functional disability (Barthel Index), functional handicap (Oxford Handicap Scale and Modified Rankin Scale), and cognitive status (orientation, clock drawing). At 1 year, mental health and health-related quality of life were assessed using the Fatigue Impact Scale, Geriatric Depression Scale, Global Deterioration Scale, and 36-item Short Form Health Survey. RESULTS: The baseline patient factors predictive of caregiver burden by multiple regression analysis were older patient age (P<0.01), male gender (P<0.05), ischemic stroke (P<0.05), urinary incontinence (P<0.0001), and impaired clock drawing (P<0.05). At 1 year, significant correlates of caregiver burden were older patient age (P<0.05), male gender (P<0.01), poor mental health (P<0.05), functional handicap (P<0.05), and functional disability (P<0.001). CONCLUSIONS: The functional status of patients can be used to identify caregivers at risk of caregiver burden. Patient demographic variables, cognitive function, and mental health status may further expose vulnerable caregivers. These factors should be considered in the development of strategies to offset caregiver burden.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".