Equity-Oriented Data Governance and Collection Approaches in Digital Mental Health and Substance Use Health Services: A Rapid Scoping Review
Bibliographic record
Abstract
This project is a rapid scoping review that investigates how equity-oriented data governance and collection approaches are being applied in the planning, design, and delivery of digital mental health and substance use health (MHSUH) services. The motivation arises from a pressing dilemma in the digital health field: balancing open, low-barrier access to care, which minimizes requirements for personal information and reduces barriers for groups facing health inequities, against the need for disaggregated sociodemographic data to inform equitable service delivery and policy. This rapid review will primarily synthesize evidence from peer-reviewed articles from three electronic databases of Medline, PsycINFO, and Embase, published between 2020 and 2025 in English and French, focusing on Canada, the United States, Australia, New Zealand, the United Kingdom, and France. We will identify: 1. Equity-oriented data governance frameworks (e.g., OCAP®, EGAP, CARE/FAIR) applied in digital MHSUH contexts. 2. Data collection practices that aim to balance privacy, accessibility, and equity monitoring. 3. Reported facilitators, barriers, and trade-offs in applying these approaches within digital MHSUH services. Expected outcomes include: 1. A map of existing equity-oriented data governance and collection practices in digital MHSUH services. 2. Identification of gaps in the existing evidence/practices and opportunities for improvement and innovation. 3. Synthesized key findings to inform a consensus-building policy dialogue with diverse community members and interested partners, including people with lived experience, community representatives, researchers, system administrators, data management experts, organizational leadership, and service providers. 4. Actionable recommendations and implementation guidelines to support the design of digital MHSUH services that are both accessible and equity-promoting. This project will provide timely, policy-relevant knowledge to help health system administrators, leaders, and users in Canada and comparable jurisdictions strike an equity-promoting balance between open access and data-informed decision-making in digital MHSUH care.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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".