Implementation of Telerehabilitation in an Early Supported Discharge Stroke Rehabilitation Program before and during COVID-19: An Exploration of Influencing Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To identify the factors influencing the implementation of telerehabilitation (TR) in a post-stroke early supported discharge (ESD) rehabilitation program as perceived by clinicians and managers. Methods: A descriptive qualitative design was used in collaboration with a Canadian ESD stroke rehabilitation program. After 15 months of pre-COVID-19 implementation and 4 months of COVID-19 implementation, 9 stakeholders (7 clinicians, 1 coordinator and 1 manager) from an ESD program participated in 2 focus groups online or an individual interview. Qualitative data were coded and analyzed semi-deductively for the pre-COVID-19 and COVID-19 phases using the Consolidated Framework for Implementation Research (CFIR). Results: Four categories emerged related to the CFIR, each with themes: (1) Telerehabilitation, which included “Technology” and “Clinical activities”; (2) Telerehabilitation users, which included: “Clients’ characteristics” and “Clinicians’ characteristics”; (3) Society and healthcare system, which included “Changes related to COVID-19” and “ESD program”; and (4) TR implementation process, which included “Planning” and “Factors that influenced practice change”. Conclusions: Factors impacting TR implementation in the ESD program were found to be numerous and varied according to the pre-COVID-19 or COVID-19 phases. Clinicians’ motivation regarding potential gains for them in using TR was key in its implementation during the COVID-19 period.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it