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Record W4389975239 · doi:10.1177/10398562231222826

Unpeeling the onion: Digital triage and monitoring of general practice, private psychiatry, and psychology

2023· article· en· W4389975239 on OpenAlexaff
Stephen Allison, Tarun Bastiampillai, Steve Kisely, Jeffrey CL Looi

Bibliographic record

VenueAustralasian Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGovernment (linguistics)TriageMental healthMental illnessHealth careDigital healthMental healthcareNursingPsychologyMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The Australian federal government is considering a 'digital front door' to mental healthcare. The Brain and Mind Centre at the University of Sydney has published a discussion paper advocating that the government should adopt a comprehensive model of digital triage and monitoring (DTM) based on a government-funded initiative Project Synergy ($30 million). We critically examine the final report on Project Synergy, which is now available under a Freedom of Information request. CONCLUSION: The DTM model is disruptive. Non-government organisations would replace general practitioners as care coordinators. Patients, private psychiatrists, and psychologists would be subjected to additional layers of administration, assessment, and digital compliance, which may decrease efficiency, and lengthen the duration of untreated illness. Only one patient was deemed eligible for DTM, however, during the 8-month regional trial of Project Synergy (recruitment rate = 1/500,000 across the region). Instead of an unproven DTM model, the proposed 'digital front door' to Australian mental healthcare should emphasise technology-enabled shared care (general practitioners and mental health professionals) for the treatment of moderate-to-severe illness.

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)0.000
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 score0.086
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.032
GPT teacher head0.388
Teacher spread0.356 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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