Validation of the InnoWell Platform: Protocol for a Clinical Trial
Bibliographic record
Abstract
BACKGROUND: New electronic health technologies are being rapidly developed to improve the delivery of mental health care for both health professionals and consumers and better support self-management of care. We developed a Web-based platform (the InnoWell Platform) that supports the prevention, early intervention, treatment, and continuous monitoring of mental health and maintenance of well-being in people aged 2 years and older. The platform is a customizable digital tool kit that operates through existing service providers who utilize the system to provide their consumers with access to evidence-based assessments and feedback, intervention options, and outcome monitoring. It does this by collecting, storing, and reporting personal and health information back to consumers and their health professionals to promote collaborative care partnerships that aim to improve the management of mental ill health and maintenance of well-being. OBJECTIVE: The aim of this study was to describe the research protocol for a naturalistic prospective clinical trial wherein all consumers presenting for care to a traditional face-to-face or Web-based mental health service in which the InnoWell Platform is being offered as part of standard clinical care will be given the opportunity to use the platform. METHODS: The Web-based platform is a configurable and customizable digital tool that assists in the assessment, monitoring and management of mental ill health, and maintenance of well-being. It does this by collecting, storing, and reporting health information back to the person and his or her clinician to enable transformation to person-centered care. The clinical trial will be conducted with individuals aged 2 years and older presenting to participating services for care, including persons from the veteran community, Aboriginal and Torres Strait Islander people, people from culturally and linguistically diverse backgrounds, the lesbian, gay, bisexual, transgender, and intersex community, and those from broader education and workforce sectors, as well as people with disabilities, lived experience of comorbidity, complex disorders, and suicidality. RESULTS: Project Synergy was funded in June 2017, and data collection began in November 2018 at a youth mental health service. At the time of this publication, 5 additional services have also begun recruitment, including 4 youth mental health services and a veteran's service. The first results are expected to be submitted in 2020 for publication. CONCLUSIONS: This clinical trial will promote access to comprehensive, high-quality mental health care to improve outcomes for consumers and health professionals. The data collected will be used to validate a clinical staging algorithm designed to match consumers with the right level of care and reduce the rate of suicidal thoughts and/or behaviors and suicide by suggesting pathways to care that are appropriate for the identified level of need, while simultaneously enabling a timely service response. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12618001676202; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374632 (Archived by WebCite at http://www.webcitation.org/78TOi5jwl). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13955.
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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 | 0.133 | 0.150 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.102 | 0.029 |
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".