RecoverNow: A patient perspective on the delivery of mobile tablet-based stroke rehabilitation in the acute care setting
Bibliographic record
Abstract
BACKGROUND/AIM: We previously reported the feasibility of RecoverNow (a mobile tablet-based post-stroke communication therapy in acute care). RecoverNow has since expanded to include fine motor and cognitive therapies. Our objectives were to gain a better understanding of patient experiences and recovery goals using mobile tablets. METHODS: Speech-language pathologists or occupational therapists identified patients with stroke and communication, fine motor, or cognitive/perceptual deficits. Patients were provided with iPads individually programmed with applications based on assessment results, and instructed to use it at least 1 h/day. At discharge, patients completed a 19-question quantitative and open-ended engagement survey addressing intervention timing, mobile device/apps, recovery goals, and therapy duration. RESULTS: Over a six-month period, we enrolled 33 participants (three did not complete the survey). Median time from stroke to initiation of tablet-based therapy was six days. Patients engaged in therapy on average 59.6 min/day and preferred communication and hand function therapies. Most patients (63.3%) agreed that therapy was commenced at a reasonable time, although half expressed an interest in starting sooner, 66.7% reported that using the device 1 h/day was enough, 64.3% would use it after discharge, and 60.7% would use it for eight weeks. Sixty-seven percent of patients expressed a need for family/friend/caregiver to help them use it. CONCLUSION: Our results suggest that stroke patients are interested in mobile tablet-based therapy in acute care. Patients in the acute setting prefer to focus on communication and hand therapies, are willing to begin within days of their stroke and may require assistance with the tablets.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".