Mental health provider and youth service users’ perspectives regarding implementation of a digital mental health platform for youth: A survey study
Bibliographic record
Abstract
Background: For youth and young adults (YYAs) with mental health concerns, digital mental health (dMH) can improve access to care and support collaboration with providers. Measurement-based care using a dMH platform may further optimize YYA outcomes by individualizing treatment approaches. Engaging service providers and YYA provides an opportunity to better understand how to mitigate implementation challenges. Aim: Explore the experiences of mental health care providers and YYAs regarding the implementation of a dMH platform for YYAs accessing mental health care in multiple care settings. Methods: Mental health care providers and YYA service users completed an electronic survey exploring their experiences and perceptions of implementing a dMH platform. Survey design, data analysis, and reporting were informed by the Consolidated Framework for Implementation Research (CFIR). Results: A total of 195 individuals (100 providers, 95 YYAs) responded. Of those, 48 providers and 79 YYAs reported using the platform at least once. Both groups identified several important factors supporting implementation including collaborative relationships between providers and YYAs, the ability to monitor mental health outcomes, and increased YYA engagement in care. The need for increased provider training and automatic reminders for YYAs to use the platform were common barriers. Each group perceived the other to be uninterested in using the platform, highlighting the importance of using all stakeholder views to inform implementation planning. Conclusions: Successful implementation of dMH for care of YYA requires ongoing, user-informed training, integration into existing workflows, and alignment with YYA priorities for care. Future work exploring provider and youth perceptions of the others' "buy-in" is needed to inform future implementation strategies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".