Unpeeling the onion: Digital triage and monitoring of general practice, private psychiatry, and psychology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".