Burden of Caregivers of People with Stroke: Evolution and Predictors
Bibliographic record
Abstract
BACKGROUND: Caregiver burden differs according to the amount of care, but no study has really focused on that point. This study compares the evolution of burden of two groups of caregivers of people with a recent stroke who returned home after discharge from two different types of health care facilities. METHODS: Burden was assessed at 3 weeks and 3 and 6 months after discharge. The two groups of people with stroke and their caregivers were recruited from acute care (n = 69) and rehabilitation facilities (n = 89). Caregivers completed a questionnaire with three dimensions. In addition to sociodemographic characteristics, we assessed variables pertaining to the clinical, physical and cognitive functioning of the people with stroke. RESULTS: Differences in burden were noted. The best predictors of burden were the caregivers' characteristics, i.e. gender (female), occupation (retired), schooling (low), age (older) and hours of care given, and the stroke survivors' characteristics, i.e. depressive symptoms, poor motor function (leg), verbal comprehension deficits, difficulty walking and neurological deficits. CONCLUSION: These results reinforce the view that services (information, training and support) should be tailored to the needs of caregivers, depending on whether or not the recipient of care has received rehabilitation services.
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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 | 0.000 | 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".