Organizational factors impacting the implementation of a digital mental health tool in Alberta's mental health care of youth and young adults
Bibliographic record
Abstract
With mental health concerns on the rise among youth and young adults (age 12-24), increased mental health options include virtual care, apps and online tools, self-management and tracking tools, and digitally-enabled coordination of care. These tools may function as alternatives or adjuncts to face-to-face models of care. Innovative solutions in the form of digital mental health (dMH) services not only provide support, resources and care, but also decrease wait times and waitlists, increase access, and empower youth. However, organizational factors may impact the extent of dMH interventions are that accepted, used, and sustained in clinical settings. This qualitative study explores organizational barriers and facilitators surrounding the implementation of a digital platform (Innowell), which uses measurement-based care (MBC) to track youth progress and outcomes. Data was collected from 154 mental health care providers participating in 23 focus groups across Alberta, drawing on school and community settings, specialized mental health services, and primary care networks. A thematic analysis revealed the following: barriers included incompatibility with current systems and workflows, lack of inter-organizational collaboration, time commitment, perceived sustainability and lack of digital literacy. Facilitators included positive attitudes towards using dMH to optimize clinical practices by empowering youth and improving continuity of care, transitions in care, and quality of care, as well as workplace culture and leadership. The study highlights a critical need for decision makers and clinical leaders to address organizational factors by integrating training and support, establishing interoperability between digitized and in-person healthcare systems, and leveraging support for MBC and youth-centred care.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".