MétaCan
Menu
Back to cohort
Record W2084088122 · doi:10.1258/1357633011936318

Telepsychiatry as a routine service - the perspective of the patient

2001· article· en· W2084088122 on OpenAlexaffabout
J Simpson, S Doze, Doug Urness, David Hailey, Philip Jacobs

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2001
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of AlbertaWinnipeg Regional Health Authority
Fundersnot available
KeywordsTelepsychiatryMedicineTelemedicineService (business)Mental healthFace-to-facePerspective (graphical)Family medicineTelephone interviewMedical emergencyNursingPsychiatryHealth careBusiness

Abstract

fetched live from OpenAlex

Patient perspectives were examined as part of an assessment of a routine telepsychiatry service in rural Alberta. Information was gathered through self-report questionnaires and telephone interviews. Of 379 questionnaires distributed to patients, 230 (61%) were returned. Of the patients who completed questionnaires, 89% reported being satisfied with the service and 96-99% were satisfied with the equipment and the room. Twenty-nine of 31 patients who were interviewed by telephone preferred telepsychiatry to waiting for a consultation, were willing to use the service again and would recommend telepsychiatry to a friend. While 25 of these 31 patients preferred telepsychiatry to travelling to a consultation, 15 indicated that they would prefer a face-to-face interview to telepsychiatry and a further seven were unsure. Twenty-three of the 31 patients interviewed would have had to miss time from work or pay for child care in order to travel to a conventional psychiatric consultation. The availability of telepsychiatry led to an estimated cost saving of $210 per consultation for patients who would otherwise have had to travel. From the patient's perspective, telepsychiatry was an acceptable technique in the management of mental health difficulties that both increased access to services and produced cost savings.

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)0.000
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 score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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

Citations65
Published2001
Admission routes2
Has abstractyes

Explore more

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