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Record W2539868930 · doi:10.1177/1357633x16674359

Delivering patient education by group videoconferencing into the home: Lessons learnt from the Telehealth Literacy Project

2016· article· en· W2539868930 on OpenAlexfundno aff
Annie Banbury, Lynne Parkinson, Susan Nancarrow, Jared Dart, Len Gray, Jennene Buckley

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2016
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersAustralian GovernmentCentral Queensland UniversityQueensland HealthUniversity Health Network
KeywordsFacilitatorTelehealthVideoconferencingMedicineMedical educationNursingIntervention (counseling)TelemedicineMultimediaPsychologyComputer scienceHealth care

Abstract

fetched live from OpenAlex

We examined the procedures for implementing group videoconference (VC) education for older people delivered into the home environment to identify the most common themes affecting the optimum delivery of VC home-based groups to older people. Participants (n = 52) were involved in a six-week group VC patient education program. There were a total of 44 sessions, undertaken by nine groups, with an average of four participants (range 1-7) and the facilitator. Participants could see and hear each other in real-time whilst in their homes with customised tablets or a desktop computer. The data presented here are based on a program log maintained by the facilitator throughout the implementation phase of the project and post intervention. The VC group experience is influenced by factors including the VC device location, connection processes, meeting times, use of visual aids and test calls. Social presence can be improved by communication protocols and strategies. Robust information technology (IT) support is essential in mitigating technical problems to enhance users' experience. Group patient education can be delivered by VC into homes of older people. However, careful pre-program planning, training and support should be considered when implementing such programs.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.340
Teacher spread0.322 · 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 designOther design
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

Citations30
Published2016
Admission routes1
Has abstractyes

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