The Urgent Need for an Evidence-Based Digital Mental Health Practice Model of Care for Youth
Bibliographic record
Abstract
Abstract Australian providers of mental health services and support for young people include private and public allied health providers, government initiatives (eg, headspace), nongovernment organizations (eg, Kids Helpline), general practitioners (GPs), and the hospital system. Over 20 years of research has established that many young people prefer to seek mental health support online; however, clear client pathways within and between online and offline mental health services are currently lacking. The authors propose a Digital Mental Health Practice model of care for youth to assist with digital mental health service mapping. The proposed model offers accessible pathways for a client to engage with digital mental health services, provides clear navigation to access support for individual needs, and facilitates a seamless connection with offline mental health services using a transferable electronic health records system. This future-looking model also includes emerging technologies, such as artificial intelligence and the metaverse, which must be accounted for as potential tools to be leveraged for digital therapies and support systems. The urgent need for a user-centered Digital Mental Health Practice model of care for youth in Australia is discussed, highlighting the shortcomings of traditional and existing online triage models evident during the COVID-19 pandemic, and the complex challenges that must be overcome, such as the integration of diverse mental health care providers and establishment of a robust electronic health records system. Potential benefits of such a model include reduced pressure on emergency rooms, improved identification of immediate needs, enhanced referral practices, and the establishment of a cost-efficient national digital mental health care model with global applicability. The authors conclude by stressing the consequences of inaction, warning that delays may lead to more complex challenges as new technologies emerge and exacerbate the long-term negative consequences of poor mental health management on the economic and biopsychosocial well-being of young Australians.
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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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".