Evaluating and co-designing telehealth in rehabilitation care: Telehealth in an early-supported discharge rehabilitation program for patients with stroke
Bibliographic record
Abstract
Acquired neurological conditions (e.g., stroke, traumatic brain injury) have high personal and economic costs impacting far beyond the patient, including the family and caregivers, community, healthcare system, and society.A 2022 report from Statistics Canada indicates that Stroke is the leading cause of adult disability; with more people surviving a stroke event, more than 875,000 Canadians are living with the effects of having had a stroke, and more than half require caregiver support to help with activities of daily living (e.g., eating, bathing, walking, transportation).Evidence-based interventions and interdisciplinary team-based care are necessary to optimize patients' recovery and optimize bodily function and social participation.As soon as a patient is medically stable, evidence-based practice research indicates that rehabilitative programs should begin.Clinical guidelines indicate the importance of early intervention following stroke with sufficient rehabilitative intervention intensity (3 hours/day) to optimize recovery.This timeline aligns with the critical time-dependent window of opportunity for neurologic recovery post-stroke.Early in recovery, 2 to 3 months post-stroke, during this critical window, there are heightened neuroplastic changes in the connection and organization of neural networks.This neuroplasticity is both spontaneous and increased through intensive rehabilitation intervention treatment resulting in improved motor recovery.Rehabilitative programs, including early supported discharge (ESD) programs, have been developed to be conducted within this critical window at a high level of intensity.These programs promote neuroplastic changes to end up with the best clinical outcome possible for the patient.ESD programs minimize secondary risk factors associated with an extended hospital stay and provide at-home interdisciplinary care from a team with multiple clinical specialties.In-home care is provided in the environment the patient will need to navigate, which is directly translational to To the Person-Centered Health Informatics Research Laboratory members, I am thankful to have crossed paths and learned from each of you.To Nicole George, my birthday twin, lab partner extraordinaire, and ice cream adventure buddy, I am out of words which I feel like you will understand, and to Catherine Giroux and Dorra Allégue, thank you for your support, encouragement, mentorship, and patience; I would not have gotten here without the three of you.Thank you to Dr. Claudine Auger and Dr. Bonnie Swaine
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".