A Bibliometric Analysis of Stroke Caregiver Research from 1989 to 2022
Bibliographic record
Abstract
Many stroke survivors suffer with varying degrees of disability and require assistance. Family members commonly act as informal caregivers, caring for these stroke survivors and ensuring care adherence. However, many caregivers reported a poor quality of life and physical and psychological distress. Due to these issues, multiple studies have been conducted to understand the experience of caregivers, the outcomes of caregiving, and interventional studies among caregivers. This study aims to explore the intellectual landscape of studies on stroke caregivers using bibliometric analysis. Studies with “stroke” and “caregiver” terms in the title were extracted from the Web of Sciences (WOS) database. The resulting publications were analysed using the ‘bibliometrix’ package in R. There were 678 publications analysed, dating from 1989 to 2022. The USA has the highest number of publications (28.6%), followed by China (12.1%) and Canada (6.1%). The most productive institution, journal and author were The University of Toronto (9.5%), ‘Topics in Stroke Rehabilitation’ journal (5.8%) and Tamilyn Bakas (3.1%), respectively. Co-occurrences keywords analysis revealed mainstream research on stroke survivors, burden, quality of life, depression, care, and rehabilitation, reflecting the timeless hotspot in the field. This bibliometric analysis helps us understand the current state of stroke caregiver research and its recent developments. This study can be used to evaluate research policies and promote international cooperation.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.205 | 0.327 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".