Exploring the Feasibility of Videoconference Delivery of a Self-Management Program to Rural Participants with Stroke
Bibliographic record
Abstract
Moving On after STroke (MOST(R)) is a multimodal, psycho-educational, and exercise self-management program for people with stroke and their caregivers. The objective of this study was to explore the feasibility of videoconference delivery to rural communities. Seven participants, their caregivers, and two facilitators formed one group, located in an urban center. Five participants and their caregivers from two remote locations were connected by videoconference. Feasibility was assessed by examining recruitment and attendance rates; program adaptations; and participant, facilitator, and staff perceptions. Data sources included logs, surveys, focus groups, and interviews. To examine preliminary outcomes, goal attainment, balance, mood, participation, and walking endurance were measured pre-, post-, and 3 months following intervention. Twelve participants were recruited in 3 weeks. Attendance rates were 89.8% for the local group and 70.4% for the remote group. Program adaptations, facilitation strategies, and involvement of onsite support promoted the success of the videoconference delivery. Participants reported that the program provided people with stroke as well as caregivers with greater awareness of stroke, increased social support, and improved ability to cope. They reported a decrease in loneliness by sharing with others in a similar situation, even if they were in a different community. Pre-post improvements were seen in goal setting, mood, balance, balance confidence, and walking endurance. Videoconferencing is a feasible method for the dissemination of the MOST program to rural areas. This form of delivery is associated with improvements in goal achievement, mood, balance, and endurance, and is well received by all participants.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".