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Record W2009935236 · doi:10.5195/ijt.2009.868

Wheelchair Seating Assessment and Intervention: A Comparison between Telerehabilitation and Face-to-Face Service

2009· article· en· W2009935236 on OpenAlexaff
Ingrid G Barlow, Lili Liu, Angela R Sekulic

Bibliographic record

VenueInternational Journal of Telerehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of AlbertaGlenrose Rehabilitation Hospital
Fundersnot available
KeywordsTelerehabilitationWheelchairPsychological interventionIntervention (counseling)Service (business)Face-to-faceAssistive technologyTelemedicinePhysical medicine and rehabilitationComputer sciencePhysical therapyPsychologyMedicineHealth careNursingBusinessHuman–computer interactionWorld Wide Web

Abstract

fetched live from OpenAlex

This study compared outcomes of wheelchair seating and positioning interventions provided by telerehabilitation (n=10) and face-to-face (n=20; 10 in each of two comparison groups, one urban and one rural). Comparison clients were matched to the telerehabilitation clients in age, diagnosis, and type of seating components received. Clients and referring therapists rated their satisfaction and identified if seating intervention goals were met. Clients recorded travel expenses incurred or saved, and all therapists recorded time spent providing service. Wait times and completion times were tracked. Clients seen by telerehabilitation had similar satisfaction ratings and were as likely to have their goals met as clients seen face-to-face; telerehabilitation clients saved travel costs. Rural referring therapists who used telerehabilitation spent more time in preparation and follow-up than the other groups. Clients assessed by telerehabilitation had shorter wait times for assessment than rural face-to-face clients, but their interventions took as long to complete.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.037
GPT teacher head0.437
Teacher spread0.400 · 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

Citations31
Published2009
Admission routes1
Has abstractyes

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