D-PATH (Data Privacy Assessment Tool For Health) for Biomedical Data Sharing
Bibliographic record
Abstract
The Data Privacy Assessment Tool for Health (D-PATH) is a proof-of-concept online tool designed to help users intending to share biomedical data identify applicable legal obligations and relevant best practices. D-PATH provides a series of simple questions to assess important aspects of the data sharing task, such as the user’s legal jurisdiction and the types of entities involved. Based on the combination of answers that the user provides, D-PATH will generate a list of privacy obligations and security-best practices, categorized into themes of 1) accountability, 2) lawfulness of storage, transfer, and protection, and 3) security and safeguards that will likely apply in the user’s scenario. Currently, the D-PATH focuses on Canadian and European privacy laws and various global best-practice policies, but there are plans to extend this in later iterations of the tool. D-PATH was developed specifically to inform users about their legal privacy obligations and best practices and was written to facilitate compliant and ethical data sharing. As a proof-of-concept, D-PATH demonstrates the potential value of a tool in simplifying and translating complex concepts into more accessible formats. Such a tool can be adapted and valuable in many different contexts, such as training core researchers in data sharing laws and practices.
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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 | 0.048 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.022 |
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".