Whose Data Are They? Data Ownership and Sovereignty in Oral Health Research
Bibliographic record
Abstract
In the complex and ever-evolving landscape of oral health research, community-based participatory research methods provide essential tools for meaningfully engaging with vulnerable and socially marginalized populations. These methods reflect community needs and integrate their perspectives into oral health research. However, issues of data control, equity, ownership, and sovereignty can lead to ethical and legal challenges. To discuss these concerns, a symposium was held in March 2025 in New York City at the American Association for Dental, Oral, and Craniofacial Research annual meeting. This event explored the intricate dynamics of data access, control, and sovereignty within the context of community-based participatory research, particularly involving vulnerable populations such as Two-Spirit, lesbian, gay, bisexual, transgender, queer or questioning, and other sexual orientations and gender identities (2SLGBTQ+), as well as Indigenous peoples, racial/ethnic minorities, and others. As oral health data become increasingly accessible across various platforms, it is incumbent on investigators to understand appropriate access, ownership, legitimate rights, and the ethical use and reuse of data to uphold equity, rights, and representation. The symposium examined the complex challenges surrounding data access, ownership, and control and their implications for community and individual rights, emphasizing the importance of implementing best practices in inclusive research and prioritizing the voices, rights, and meaningful integration of vulnerable populations. Speakers presented and advocated for multifaceted frameworks that integrate cultural values and traditions, aiming to promote equitable oral health outcomes. The symposium also underscored the critical role of ethical data stewardship in big data and community-based oral health research in American Indian, Hispanic, and Global East African contexts. Case studies showcased collaborative approaches that meaningfully engage community stakeholders and service users throughout the research process, ensuring that data are utilized ethically and yield genuine benefits for the populations involved.Knowledge Transfer Statement:This symposium emphasized the critical role of data ownership and sovereignty in advancing oral health equity, particularly for socially marginalized groups such as 2SLGBTQ+ communities, Indigenous peoples, racial minorities, and others. It highlighted best practices for ethical data stewardship and inclusive research that centers community voices. The session offered actionable frameworks to help researchers, policy makers, and institutions build trust, integrate community values, and ensure culturally sensitive outcomes in their efforts to advance health equity.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.211 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.017 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".