An Evaluation of the Family Informal Caregiver Stroke Self-Management Program
Bibliographic record
Abstract
BACKGROUND: Caregivers are often unprepared and overwhelmed with the responsibilities of providing care to stroke survivors, which can lead to negative physical and psychological effects. PURPOSE: To evaluate the impact of the Family Informal Caregiver Stroke Self-Management (FICSS) program on burden and life changes resulting from providing care among family caregivers of stroke survivors. METHODS: A prospective pre-test and post-test design using quantitative and qualitative data was used to evaluate the program with a convenience sample of 42 caregivers. The four-module facilitated program consisted of small group-guided discussion. Quantitative evaluations were completed at baseline, 2 weeks and 6 months (post-intervention), and qualitative data were collected at 2 weeks and 6 months. Life changes and burden were measured using the Bakas Caregiving Outcome Scale (BCOS) and the Oberst Caregiving Burden Scale (OCBS), respectively. RESULTS: The BCOS scores increased consistently over time, showing significant differences at 6 months compared with 2 weeks (mean difference: 5.29, 95% confidence interval [CI]: 0.30-10.28, p=0.04) and baseline (mean difference: 7.58, 95% CI: 2.92-12.23, p=0.001). The OCBS time scores decreased consistently over time, showing a significant difference at 6 months compared with baseline (mean difference: -5.20, 95% CI: -0.96 to -9.44, p=0.02). The OCBS difficulty scores fluctuated over time, resulting in no overall difference from baseline to 6 months. Qualitative themes were consistent with the positive quantitative findings. CONCLUSION: Study results suggest that the FICSS program may result in reduced caregiver burden and improved life changes resulting from providing care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".