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Record W2061325757 · doi:10.1258/135763306777889028

Client acceptability and quality of life – telepsychiatry compared to in-person consultation

2006· article· en· W2061325757 on OpenAlexaff
Doug Urness, Malin Wass, Alan Gordon, Esther Tian, Tim Bulger

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2006
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsAlberta Hospital Edmonton
Fundersnot available
KeywordsTelepsychiatryMental healthFeelingMedicineMental health serviceMental health carePatient satisfactionFace-to-faceTelemedicinePsychologyPsychiatryFamily medicineNursingHealth careSocial psychology

Abstract

fetched live from OpenAlex

We evaluated client satisfaction and one-month mental health outcomes for telepsychiatry clients compared with those undergoing a face-to-face psychiatric consultation. Clients were asked to complete an SF-12 health survey before the consultation, a satisfaction survey after the consultation, and were contacted for a one-month follow-up SF-12 survey by telephone. Forty-eight of the 62 initial responders (77%) were available for contact by telephone after one month. Telepsychiatry clients demonstrated significant improvements on pre- and post-SF-12 mental health measures (t = 3.7; P = 0.001), while there was no change for the in-person group (t = 1.0; P = 0.35). Telepsychiatry clients felt that they could present the same information as in person (93%), were satisfied with their session (96%), and were comfortable in their ability to talk (85%); this was similar to the in-person clients. They reflected slightly lower levels of satisfaction regarding feeling supported and encouraged than did the in-person clients. Both telepsychiatry clients and traditional face-to-face psychiatry clients were satisfied with their experience of mental health care service provision, and mental health improvements were evident in the telepsychiatry patients.

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 score0.026
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.364
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 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

Citations59
Published2006
Admission routes1
Has abstractyes

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