The effect of a students led caregiving support program on depression, sleep quality and quality of life of the caregivers and patients with stroke
Bibliographic record
Abstract
Purpose We aimed to provide care support to stroke patients and their caregivers and investigate this support's impact on the psychosocial characteristics of patients', caregivers', and volunteer students' depression, quality of life, and sleep quality.Material/Methods Volunteer students received caregiving training and provided support to caregivers at patients' homes. Caregivers received care support through the project for four sessions, once a week. Depression levels were assessed using the Beck Depression Scale, quality of life with RAND-36, sleep quality with the Pittsburgh Sleep Quality Index.Results The study included 15 patients after stroke, 15 caregivers, and a total of 30 students participated in delivering the intervention. We observed that physical function (d = 0.52) and social function (d = 0.67) showed significant improvements with moderate effect sizes in caregivers. In patients, Energy/Vitality (d = 0.86) showed a significant and large effect size, and pain reduction (d = 0.78) demonstrated a significant, moderate effect size. For students, Emotional Role Difficulty had a significant improvement with a moderate effect size.Conclusions Overall, the findings suggest that the project led to meaningful benefits for all involved, particularly in enhancing quality of life parameters. It was found that all participants were extremely satisfied with the study and thought that it should be expanded.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".