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Record W2046297412 · doi:10.1258/jtt.2008.080415

Assessing interprofessional teamwork in a videoconference-based telerehabilitation setting

2008· article· en· W2046297412 on OpenAlexaff
Emmanuelle Careau, Claude Vincent, Luc Noreau

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2008
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsVideoconferencingTelerehabilitationTeamworkTelehealthDisadvantageTelemedicineMedicineRehabilitationComputer scienceHealth careMultimediaPhysical therapy

Abstract

fetched live from OpenAlex

We studied the workings of a rehabilitation team in a videoconference setting to note the pros and cons of videoconferencing in the development of interprofessional care plans (ICPs). We recorded every videoconference held by the teams of the specialized centre and the regional centre for clients with traumatic brain injuries over an 18-month period. Thirteen recorded videoconferences, lasting for 30-98 min, were analysed through an observation grid. On the whole, efficient teamwork was observed: the mean productivity level was 96%, while the percentage of time dedicated to the resolution of technical issues was 2%. During the videoconferences, the clinical coordinator and the client addressed the group most often. One of the most commonly mentioned advantages was the good visual contact provided by videoconferencing. The most often quoted disadvantage was the poor sound quality. The findings from the study support the adoption of videoconferencing and suggest a few guidelines for the development of ICPs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.378
Teacher spread0.339 · 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

Citations25
Published2008
Admission routes1
Has abstractyes

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