Technology-Enabled Mental Health Service Reform for Open Arms – Veterans and Families Counselling: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: The impact of mental ill-health on every aspect of the lives of a large number of Australian Defence Force (ADF) personnel, their partners, and their families is widely recognized. Recent Senate inquiries have highlighted gaps in service delivery as well as the need for service reform to ensure appropriate care options for individuals who are currently engaged with mental health and support services as well as for those who, for a variety of reasons, have not sought help. To that end, successive Australian governments generally and the Department of Veterans' Affairs specifically have prioritized veteran-centric reform. Open Arms is an Australia-wide service that provides counseling and support to current and former ADF personnel, and their family members, for mental health conditions. OBJECTIVE: The aim of this study was to develop and configure a prototypic Web-based platform for Open Arms - Veterans & Families Counselling (formerly Veterans and Veterans Families Counselling Service) with the Open Arms community to enhance the quality of mental health services provided by Open Arms. METHODS: The study aimed to recruit up to 100 people from the Open Arms community (current and former ADF personnel and their families, health professionals, service managers, and administrators) in regions of New South Wales, including Sydney, Canberra, Maitland, Singleton, and Port Stephens. Participants were invited to participate in 4-hour participatory design workshops. A variety of methods were used within the workshops, including prompted discussion, review of working prototypes, creation of descriptive artifacts, and group-based development of user journeys. RESULTS: Seven participatory design workshops were held, including a total of 49 participants. Participants highlighted that the prototype has the potential to (1) provide the opportunity for greater and better-informed personal choice in relation to options for care based on the level of need and personal preferences; (2) ensure transparency in care by providing the individual with access to all of their personal health information; and (3) improve collaborative care and care continuity by allowing information to be shared securely with current and future providers. CONCLUSIONS: Our findings highlight the value of actively engaging stakeholders in participatory design processes for the development and configuration of new technologies.
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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 | 0.043 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".