Determinants of equitable data governance for ACB communities in health research in high income countries: A scoping review protocol
Bibliographic record
Abstract
We will conduct a scoping review on the current practices that exist among research organizations and ACB communities on data governance, ownership and control over health data. Our national expert group acknowledge the existing health disparities faced by ACB communities for generations rooted in anti-Black racism. This has led to power differentials among ACB community members and various health and research institutions, limited access to health data, limited development of targeted population interventions. This notable gap continues to create tension in data governance, access, control and ownership among researchers and ACB communities. Therefore, the aim of this scoping review is to examine determinants of equitable data governance for ACB communities in health research in HICs. The findings from this review will be the catalyst to develop a data governance framework for ACB populations in Canada.
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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 | 0.253 | 0.242 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".