Perceptions of mental health providers of the barriers and facilitators of using and engaging youth in digital mental-health-enabled measurement based care
Bibliographic record
Abstract
Objectives Increased rates of mental health disorders and substance use among youth and young adults have increased globally, furthering the strain on an already burdened mental health system. Digital solutions have been proposed as a potential option for the provision of timely mental health services for youth, with little research exploring mental health professional views about using such innovative tools. In Alberta, Canada, we are evaluating the implementation and integration of a digital mental health (dMH) platform into existing service pathways. Within this paper we seek to explore mental health professionals’ perceptions of the barriers and facilitators that may influence their utilization of digital MH-enabled measurement-based care (MBC) with the youth who access their services. Methods A qualitative, descriptive methodology was used to inductively generate themes from focus groups conducted with mental health professionals from specialized mental health services and primary care networks in Alberta. Results As mental health professionals considered the barriers and facilitators of using dMH with youth, they referenced individual and family barriers and facilitators to consider. Providers highlighted perceived barriers, including: first, cultural stigma, family apprehension about mental health care, and parental access to dMH and MBC as deterrents to providers adopting digital platforms in routine care; second, perceptions of increased responsibility and liability for youth in crisis; third, perception that some psychiatric and neurodevelopmental disorders in youth are not amenable to dMH; fourth, professionals contemplated youth readiness to engage with dMH-enabled MBC. Participants also highlighted pertinent facilitators to dMH use, noting: first, the suitability of dMH for youth with mild mental health concerns; second, youth motivated to report their changes in mental health symptoms; and lastly, youth proficiency and preference for dMH options. Conclusions By identifying professionals’ perceptions of barriers and facilitators for youth users, we may better understand how to address misconceptions about who is eligible and appropriate for dMH through training and education.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".