Feasibility of mHealth Integration into Integrated Youth Services: A Secondary Data Analytics Approach
Bibliographic record
Abstract
Background: Integrated youth services (IYS) provide low-barrier, accessible, interdisciplinary mental health and substance use (MHSU) services to youth in order to improve their social and health outcomes. Foundry, an IYS in British Columbia, Canada, launched virtual services in 2020, along with the Foundry BC app, to provide accessible services to all youth ages 12-24. Services include virtual counselling, peer support, physical/sexual health care, and work/study support. Using the app, youth have a choice of audio, video, or chat sessions with an integrated care team. Despite the promising growth of the service at Foundry, an evaluation of the service has not yet taken place. Objective: The objectives of our study were to 1) measure the extent to which the demographic and health profiles of youth who accessed Foundry BC app and in-person services differed; 2) understand the extent to which youth who accessed the Foundry BC App engaged in scheduling and receiving sessions with service providers; and 3) compare the types of services and rates of registrations in physical Foundry centres to those accessed Foundry BC app. Methods: Data on young people accessing Foundry services across physical centres and Foundry Virtual BC who have completed the Foundry health survey were analyzed for this study. Descriptive statistics analytics were used to understand trends and compare data in registrations and health outcomes. Chi-square tests were used to determine whether demographic categories are associated with the type of service selected. Results: Preliminary data analysis is currently underway, and results will be presented at ICIC24. Initial assessment of the data indicates a promising exponential trend in registration in the app-based virtual service compared to physical centre registration, along with differing demographic profiles. Future analysis on service type and service utilization pattern will identify distinct mHealth service needs in IYS and establish guidelines to inform future service design and improvement. Significance: This study is producing knowledge on the sociodemographic and health characteristics associated with youth mobile health (mHealth) service use, allowing the refinement of targeted care and quality improvements to the service itself. The feasibility and sustainability of growth of the service also suggests the value added of the App being integrated as a core service option for youth in Canada. The integration of mHealth services also serve as a potential solution to reduce health inequities especially in remote, indigenous and other vulnerable population. The implications of this research extend to the broader field of integrated care in various global setting, offering a valuable perspective on leveraging technology to improve accessibility and effectiveness of care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".