Lessons Learned from Co-Designing a Digital Health App for Foster Youth: Development and Usability Study
Bibliographic record
Abstract
Background: Foster youth experience high rates of unmet mental health and substance use needs, while simultaneously facing numerous barriers to accessing and engaging in community-based services. Behavioral intervention technologies (BITs) are promising for overcoming some of the barriers to service engagement, particularly when designed in collaboration with the intended users. Objective: This study describes lessons learned from a 31-month process of co-designing FostrSpace (Chorus Innovations, Inc), a BIT to address social determinants of health and behavioral health needs among foster youth. Our overall aim is to provide a roadmap for other scholars wishing to co-design BITs with minoritized youth that have the potential to address social determinants of health and increase access to and engagement in behavioral health care. Methods: The co-design process of creating FostrSpace included 5 phases: design, development, launch, testing and evaluation, and iterative refinement. We describe the activities conducted during each phase, as well as the resultant FostrSpace application. In-application FostrSpace usage data were collected as part of a quality improvement effort to iteratively refine the application; during registration, all youth signed a user agreement that detailed data usage. Results: FostrSpace usage data were collected from 40 youth (32/40, 78% aged 18-26 years; 8/40, 20% 13-17 years). Based on the resource needs checklist at sign-up, youth sought resources in the domains of emotional wellness (29/40; 72.5%), health care (17/40, 42.5%), housing (16/20, 40%), transportation (15/40, 37.5%), employment (15/40, 37.5%), school (13/40, 32.5%), food (12/40, 30%), family (11/40, 27.5%), and legal (7/40, 17.5%) resources, or other or not sure (16/20, 40%). Fifteen youth accessed support from the personal care navigator. Fourteen youth completed the emotional wellness questionnaire (EWQ) and identified substance use, depression, anger and irritability, mania, anxiety, somatic symptoms, and sleep problems as areas of concern. Seven of these youth initiated behavioral health services with a FostrSpace clinician. Conclusions: Engaging in participatory co-design of BITs with foster youth and other minoritized communities requires careful attention to power dynamics. Creating a space where co-designers feel there is mutual benefit to engaging in the process and it is psychologically safe to share their experiences is crucial for success. We describe lessons learned from engaging in this co-design work, including how it relates to decisions about the technology (eg, balancing youth privacy with the burden of the login process), working with third-party developers (eg, ensuring technology development partners have sufficient knowledge about the population you are co-designing with to meaningfully engage with them), and considerations for the strategic embedding of technology-based interventions within existing systems of care to promote uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".