Exploring the Determinants and Experiences of Senior Stroke Patients with Virtual Care
Bibliographic record
Abstract
OBJECTIVE: The study sought to explore the experiences of participants affected by stroke with home video visit (HVV) for follow-up visits in order to understand the determinants, barriers, and benefits associated with HVVs. METHODS: Semi-structured interviews were conducted with (n = 23) participants to gather insight and descriptive information about patients' experiences with HVV. Specifically, we sought to collect descriptions about the (1) costs and time associated with in-person visits, (2) facilitators and barriers to in-person and virtual visits, and (3) their values attached to traditional and virtual forms of patient care. RESULTS: HVVs were perceived to be a mode of healthcare that is time-saving and convenient for both participants and physicians. However, our study also found some participants felt uncomfortable using technology to conduct medical visits while others still supported a positive view of traditional forms of in-person visits because they valued the in-person interactions and safe environment of the hospital. CONCLUSION: While HVVs were considered to be useful in addressing geographical barriers to health care, technological and digital health literacy may serve to impede seniors from using the service, with some of them opting to go to the hospital despite geographical barriers. Resultantly, HVVs may serve both to alleviate and exacerbate certain determinants to health 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".