MétaCan
Menu
Back to cohort
Record W2087132287 · doi:10.1258/135763307779701239

Perception of eye contact in video teleconsultation

2007· article· en· W2087132287 on OpenAlexaff
Tony Tam, Joseph A Cafazzo, Emily Seto, Mary Ellen Salenieks, Peter G. Rossos

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2007
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsVideoconferencingEye contactGazeEye trackingPerceptionOptometryTelehealthTelemedicineEye movementComputer scienceMedicineComputer visionPsychologyAudiologyArtificial intelligenceCommunicationMultimediaHealth care

Abstract

fetched live from OpenAlex

During patient consultations by videoconferencing, clinicians often sit as close as 1 m from the videoconferencing units, creating a significant eye gaze angle (i.e. the angle between the eye and the camera, and the eye and the centre of the display). Eye gaze angle may adversely affect the satisfaction with videoconferencing. Four videoconferencing environments were examined: desktop PC, portable telehealth unit, videoconferencing room, and a boardroom equipped with a ceiling-mounted camera and a projection screen. Two still images of each of the three subjects were captured: one at a 7 degrees eye gaze angle and the other at 15 degrees. Each of 53 observers ranked four pairs of images for each of the three subjects. In 87% of cases, the observers perceived better eye contact at an eye gaze angle of 7 degrees than 15 degrees. Also, 92% of observers responded that the difference in the perceived eye contact was important to them as patients. Improved eye contact can be realized by increasing the horizontal distance of participants from the videoconferencing unit.

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)0.002
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 score0.245
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.017
GPT teacher head0.349
Teacher spread0.332 · 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

Citations48
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Telemedicine and TelecareSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207