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Record W2097893875 · doi:10.1258/13576330260440754

The evolution of a successful telemedicine mental health service

2002· article· en· W2097893875 on OpenAlexaffabout
David Hailey, Tim Bulger, Sharlene Stayberg, Douglas Urness

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2002
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTelemedicineTelepsychiatryTelehealthMental healthGovernment (linguistics)Service (business)BusinessMedicineNursingService delivery frameworkHealth careMarketingPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Development of telemedicine mental health services in Alberta evolved via a pilot project, the delivery of routine services to a small group of centres and subsequent expansion to a province-wide programme. Success of the service was linked to support for telehealth by the provincial government and consultation between the Alberta Mental Health Board (AMHB) and local stakeholders. Assessments by the AMHB have shown that telepsychiatry is acceptable and sustainable at a realistic cost. However, there are few measures of clinical effectiveness available and none of cost-effectiveness. A detailed economic evaluation of the telemedicine mental health network would now be a major task. The expansion of telemedicine mental health services has increased the expectations of health-care decision makers. In addition, the complexity of the network has increased and new initiatives, such as the use of telepsychology, have been introduced. Management of this successful telehealth programme continues to be time consuming and challenging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.670

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.318
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2002
Admission routes2
Has abstractyes

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