Training volunteers to support a tablet-based aphasia program with inpatients in a stroke rehabilitation unit: A win–win situation
Bibliographic record
Abstract
Meeting rehabilitation intensity goals in an inpatient stroke rehabilitation unit is a constant challenge due to staffing and budget constraints, a situation further exacerbated by the COVID-19 pandemic. To bridge the gap, speech-language pathologists often provide independent practice (traditionally paper and pen exercises and more recently tablet-based); however, patients can have trouble completing these programs independently. Caregivers can help but may be overwhelmed during the inpatient rehabilitation process. In this case study, we enlisted student volunteers from a local health-related program to offer individualized supplemental tablet-based aphasia practice as an adjunct to standard therapy with a speech-language pathologist for people with aphasia. Five adults with aphasia (2 women and 3 men; aged 37 to 80, mean age 58.2) were admitted to an inpatient stroke rehabilitation unit and received supervised sessions during off-therapy hours. Participants attended 57/62 (91.9%) of available sessions, ranging from 5 to 24 sessions per participant. Mean session duration per participant ranged from 56 to 92 minutes. All student volunteers remained engaged over 5 months with no attrition. This program shows that volunteers can support the delivery of a tablet-based program as an adjunct to conventional aphasia therapy to boost rehabilitation intensity during an inpatient stroke rehabilitation admission. [198 words].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".