It's better than telephone, and it's better than driving to Thunder Bay : clients' perceptions of experiences in participating in a group-based stroke self-management program using videoconference technology
Bibliographic record
Abstract
PURPOSE: Videoconference is used in rural and remote areas to improve access to healthcare, including individual clinical assessments and the delivery of group education. Moving On after Stroke (MOST? ) is a group-based, self-management program for stroke survivors and their caregivers, which consists of information sharing, facilitated discussion, goal-setting, and exercise. The program was delivered simultaneously to local participants onsite and distant participants using videoconferencing (MOST-Telehealth Remote). This research was designed to learn about the experiences of the remote participants, their perceptions regarding perceived enablers and barriers to videoconference participation, and suggestions for improvement. FINDINGS: All participants valued accessible programming without having to travel long distances. Many reported "feeling as if they were in the same room" but also acknowledged that there were technical limitations when participating via videoconference. They recognized a loss of subtleties in communication. Factors facilitating engagement and participation were similar to factors in face-to-face groups and included: program content; having a skilled group leader; and having a connection to another group member, rather than the videoconference environment itself. The importance of onsite coordinators, volunteers, and the presence of other local participants were highlighted. Suggestions for improved group cohesion and participation included having a preliminary face-to-face meeting, implementing technical strategies, and having onsite support.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".