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Record W4417330187 · doi:10.1080/10400435.2025.2599816

Training volunteers to support a tablet-based aphasia program with inpatients in a stroke rehabilitation unit: A win–win situation

2025· article· en· W4417330187 on OpenAlexaff
Daniel McEwen, Thi Cao, Hillel M. Finestone

Bibliographic record

VenueAssistive Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of OttawaBruyère
Fundersnot available
KeywordsRehabilitationAphasiaStroke (engine)StaffingAdjunctSession (web analytics)Occupational therapy

Abstract

fetched live from OpenAlex

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].

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.321
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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