Implementation of Telerehabilitation in an Early Supported Discharge Stroke Rehabilitation Program before and during COVID-19: An Exploration of Influencing Factors
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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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".