621. MENTAL HEALTH AUSTRALIA GENERAL CLINICAL TRIAL NETWORK (MAGNET) – CONNECTING KNOWLEDGE FOR BETTER MENTAL HEALTH OUTCOMES
Bibliographic record
Abstract
Abstract Background Mental Health Australia General Clinical Trials Network (MAGNET) is the first network in Australia dedicated to clinical trials in adult mental health, aiming to advance prevention, diagnosis, treatment, and recovery. Funded through the 2020 Million Minds Mission grant by the Australian Medical Research Future Fund (MRFF), MAGNET’s goal is to facilitate large-scale, coordinated clinical trials at both national and international levels. By fostering collaboration, MAGNET seeks to drive transformative mental health research, addressing critical unmet needs and answering complex questions that require the scale and expertise of a unified network. Aims & Objectives MAGNET's overarching vision is to be a leading network internationally for high-quality mental health clinical trials that serve community needs. MAGNET’s mission is to unify and improve adult mental health clinical trial research and translation in Australia. MAGNET aims to create a reusable, sustainable and shared infrastructure to strengthen the capacity, quality, effectiveness, and translation of mental health clinical trials (CTs). Method MAGNET is a collaboration between over 50+ Australian institutions and 600+ leading researchers, clinicians, and lived experience research partners in mental health aiming to improve the quality, capacity, reach and translation of clinical trials. Results In collaboration with the mental health community, MAGNET has established seven platform resources designed to enhance the quality and capacity of clinical trials. MAGNET also supports four signature trials, leveraging these platforms to provide essential resources and expertise for trial design and implementation while minimising duplication. MAGNET has established a trial endorsement process to support transformative mental health research, targeting critical gaps and unresolved questions at both national and international levels. At the core of MAGNET’s governance are Lived Experience Research Partners, First Nations collaborators, researchers, and clinicians, whose active involvement is essential across all aspects of the network. Through strategic planning, MAGNET is committed to ensuring that clinical trials drive significant progress in prevention, diagnosis, treatment, and recovery worldwide. Discussion & Conclusions MAGNET is dedicated to enabling scalable, high-quality, and translatable mental health clinical trials that meet community needs. The network warmly invites national and international researchers and partners to collaborate in the development of mental health clinical trials.
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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 | 0.189 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.119 | 0.041 |
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".