Centering equity, diversity, and inclusion in youth digital mental health: findings from a research, policy, and practice knowledge exchange workshop
Bibliographic record
Abstract
Background: Youth mental health service organizations continue to rapidly broaden their use of virtual care and digital mental health interventions as well as leverage artificial intelligence and other technologies to inform care decisions. However, many of these digital services have failed to alleviate persistent mental health disparities among equity-seeking populations and in some instances have exacerbated them. Transdisciplinary and intersectional knowledge exchange is greatly needed to address structural barriers to digital mental health engagement, develop and evaluate interventions with historically underserved communities, and ultimately promote more accessible, useful, and equitable care. Methods: To that end, the Digital, Inclusive, Virtual, and Equitable Research Training in Mental Health Platform (DIVERT), the Maritime Strategy for Patient Oriented Research (SPOR) SUPPORT (Support for People and Patient-Oriented Research and Trials) Unit and IWK Mental Health Program invited researchers, policymakers, interprofessional mental health practitioners, trainees, computer scientists, health system administrators, community leaders and youth advocates to participate in a knowledge exchange workshop. The workshop aimed to (a) highlight local research and innovation in youth-focused digital mental health services; (b) learn more about current policy and practice issues in inclusive digital mental health for youth in Canada, (c) participate in generating action recommendations to address challenges to inclusive, diverse and equitable digital mental health services, and (d) to synthesize cross-sector feedback to inform future training curriculum, policy, strategic planning and to stimulate new lines of patient-oriented research. Results: Eleven challenge themes emerged related to white-colonial normativity, lack of cultural humility, inaccessibility and affordability of participating in the digital world, lack of youth and community involvement, risks of too much digital time in youth's lives, and lack of scientific evidence derived from equity-deserving communities. Nine action recommendations focused on diversifying research and development funding, policy and standards, youth and community led promotion, long-term trust-building and collaboration, and needing to callout and advocate against unsafe digital services and processes. Conclusion: Key policy, training and practice implications are discussed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.071 |
| Research integrity | 0.000 | 0.002 |
| 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".