A Novel Digital Platform to Support Child and Family Mental Health in Australia (Child and Family eHub): Protocol for a Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: Child mental health disorders are a significant Australian public health issue with high prevalence rates compounded by inequitably higher rates for those living in families with lower income, lower levels of parental education, and higher levels of unemployment. Prevention and early intervention approaches are critical to address problems early. When caregivers seek information and services to support their child's mental health needs, they commonly use many untested online search strategies. To address this, we developed a digital Child and Family eHub (eHub) prototype through a user-centered design process involving families experiencing adversity and local service providers. The eHub provides online navigation and evidence-based information for families and aims to increase equitable access to and use of (1) information and (2) the existing primary health, mental health, and social services system to improve mental health outcomes for caregivers with children aged 0-12 years. This protocol outlines how we will evaluate the eHub. OBJECTIVE: This study aims to evaluate the feasibility, acceptability, appropriateness, and preliminary impact of the eHub digital platform for caregivers of children aged 0-12 years, particularly those experiencing adversity. The evaluation will assess implementation outcomes, caregiver and child mental health outcomes, and help-seeking behaviors over a 6-month period. METHODS: A prospective cohort of 270 caregivers of children aged 0-12 years will be recruited from 3 socioeconomically diverse Australian sites (Wyndham Vale in Victoria and Marrickville and Fairfield in New South Wales). Participants will be recruited through local community hubs, health and social service providers, and social media, and will enroll via the REDCap (Research Electronic Data Capture; Vanderbilt University) survey platform. A mixed methods type 3 implementation impact evaluation will be undertaken, which tests an implementation strategy while observing and gathering information on the intervention's impact on relevant outcomes. In this protocol, implementation will be assessed as a primary outcome using Proctor's outcomes for the implementation framework, and secondary outcomes will include caregiver access and use of the eHub and associated child and parent mental health outcomes. Data will be collected at baseline and 6 months. Quantitative data will be analyzed using descriptive statistics and regression models; repeated measures will be analyzed using generalized estimating equations. Qualitative data will be analyzed using framework analysis. RESULTS: The study was funded in December 2021. Participant enrollment for the study began in February 2024, with participants involved in the eHub evaluation for 6 months. CONCLUSIONS: The results of this study will be instrumental in refining the intervention for future scaling to other Australian sites. This study has the potential to offer an accessible, cost-effective, and scalable digital solution to improve service navigation and mental health outcomes for children and families experiencing adversity. TRIAL REGISTRATION: ISRCTN Registry ISRCTN49839991; https://doi.org/10.1186/ISRCTN49839991. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72548.
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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 | 0.063 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".