Unlocking mobile health adoption: A qualitative exploration of user experiences, barriers, and facilitators within integrated youth services in British Columbia, Canada
Bibliographic record
Abstract
Background: Early onset of mental health disorders is common, but many cases remain undetected and untreated, highlighting the need for early intervention. In Canada, youth mental health services face challenges, including fragmentation and resource limitations. Integrated youth services (IYS) aim to address these gaps for individuals aged 12-24 years. Mobile health (mHealth) programs, like Foundry Virtual BC, offer potential solutions, yet their integration and sustainability within IYS require further exploration. Objective: This study examined interest-holder perspectives on creating a sustainable, youth-centred mHealth system to improve mental health outcomes. The research focused on three questions: (a) How do users, service providers, and nonclinical staff perceive mHealth's effectiveness and impact? (b) What are the barriers and facilitators to mHealth integration within the Foundry IYS network? and (c) What strategies support the sustainability of mHealth services? Methods: A qualitative study using semi-structured interviews was conducted with 23 interest-holders, including youth users, service providers, and nonclinical staff from the Foundry network. Participants were recruited via social media and snowball sampling. Thematic analysis identified key themes and subthemes. Results: Three themes emerged regarding mHealth perceptions: (a) its own value, (b) its potential to address barriers to in-person care, and (c) its inherent limitations. Barriers and facilitators of mHealth integration were categorized into three domains: (a) design characteristics (e.g., app usability and content quality), (b) individual youth factors (e.g., privacy concern and inner struggle), and (c) external factors (e.g., safe space and support from peers). Sustainability was linked to service quality and external support. Conclusion: This study highlights the complexity of mHealth integration within an IYS network. Interest-holders emphasized addressing user motivations, privacy, and accessibility while advocating for co-design approaches to ensure mHealth meets diverse youth needs. Future research should focus on underrepresented groups to promote equity and improve mental health outcomes through sustainable mHealth solutions.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".