Co-created data governance frameworks for youth mental healthcare: Values, principles, and implementation—A scoping review
Bibliographic record
Abstract
Background: The collection, storage, and use of data are essential elements for advancing mental health services, clinical care, and policy. Data governance is the framework of rules and processes that guide decisions impacting how data are stored, accessed, and controlled, and is foundational for the ethical management of data overall. As digital data practices grow, governance models must reflect the values of different communities. This scoping review aims to (1) understand the values and principles that are important to youth in the governance of their mental health data, (2) identify existing implementations of co-created frameworks of values and principles for data governance, and (3) explore opportunities for integration with existing data governance frameworks. Methods: The recommended scoping review methodology by Arksey and O'Malley, alongside updated methodology by Peters et al., was followed. We searched four databases (Scopus, MEDLINE (Ovid), Embase (Ovid), and APA PsycINFO (Ovid)) on June 7, 2024 for records from 2013 to present. Results: Of 23 included studies, only four explicitly explored youths' preferences for mental health data use and co-creation of governance frameworks. None focused specifically on youths' data-related values, and few addressed issues of data governance. Important themes emerged around privacy, trust, transparency, and control. Conclusion: Findings suggest that urgent attention is required to improve data governance for youth receiving mental health services. In particular, the collaborative development of values and principles for governance frameworks of youth mental health data should be central when developing youth-centered services that collect and use these data.
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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 | 0.111 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".