A Systematic Review of Caregiver Burden following Stroke
Bibliographic record
Abstract
Abstract Caregiver burden following stroke is increasingly recognised as a significant health care concern. A growing number of studies have evaluated the patient, caregiver, and social support factors that contribute to increased caregiver burden. We conducted a systematic review of this literature to guide future research. A search of the MEDLINE, PsycINFO, CINAHL, and EMBASE databases (up to July 2008) and reference sections of published studies using a structured search strategy yielded 24 relevant articles. Studies were included if they evaluated predictors and/or correlates of caregiver burden in the setting of stroke. The prevalence of caregiver burden was 25-54% and remained elevated for an indefinite period following stroke. In studies that evaluated independent baseline predictors of subsequent caregiver burden, none of the factors reported were consistent across studies. In studies that assessed concurrent factors independently contributing to caregiver burden in the poststroke period, patient characteristics and social support factors were inconsistently reported. Several studies identified caregiver mental health and the amount of time and effort required of the caregiver as significant determinants of caregiver burden. Our findings highlight the need for more research to identify caregivers in need of support and guide the development and implementation of appropriate interventions 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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".