Pilot evaluation of the family informal caregiver stroke self-management program.
Bibliographic record
Abstract
BACKGROUND: Due to the abrupt onset of a stroke, caregivers are often unprepared for the role, have extensive needs and are at risk for negative outcomes. Interventions facilitating development of coping and problem solving skills are shown to decrease the negative effects of caregiving. The Family Informal Caregiver Stroke Self-Management (FICSS) program was developed to address the unmet needs of family caregivers of stroke survivors. OBJECTIVE: To evaluate the content, format and resource materials of the FICSS program with 10-12 family caregivers of stroke survivors within Central South Ontario. METHODS: The FICSS program is a four-module education and support series of facilitated small group discussions. Topics included: balancing changing roles, managing behaviour changes, sexuality/intimacy, and community linkages. Each module was evaluated weekly and by focus group one month after the program. RESULTS: Eleven family caregivers of stroke survivors with three years of mean caregiving experience participated. Caregivers valued the opportunity to share experiences and learn from others who understood what they were going through. Recommendations were made to improve the program topic areas, content and flow. CONCLUSIONS: The pilot evaluation of the program validated the content of the FICSS program and indicated recommendations for improvement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".