Designing Dynamic Informed Consent For Public Health Research
Bibliographic record
Abstract
Abstract Background Current dynamic consent software available is not all tailored for public health research and lacks the appropriate data governance framework and mechanisms to be compliant with public health research requirements. This paper presents the UML modelling of the Dynamic Consent for Public Health (DC4PH), a dynamic consent platform being designed for the Canadian public health domain Methods The proposed platform incorporates data governance and public health stakeholders’ requirements into its design and is based on state-of-the-art standards and solutions. Diagrams created in UML reflect the mapped requirements and demonstrate the capabilities of our dynamic consent proposed data structure. We also incorporated private ledger entities in our modelling to provide an immutable log of dynamic consent for stakeholders and improve transparency and trust between them. Results A series of UML assets were created based on previous mapping of the public health research domain, public health principles, and data governance requirements. Use case diagrams for each type of user of the platform, state diagrams of key business objects vital for the platform's enhancement of traditional consent, and state diagrams of the actor's possible states. Flowchart of the core platform's functionalities, such as the dynamic consent approval process. And a class diagram of the proposed platform that incorporates all the previously mentioned domain mapping and binds it with optional private ledger properties. Conclusions This work offers a comprehensive and extensible UML design that is being used to develop the DC4PH platform. The paper concludes that the design has the potential to improve the efficiency and efficacy of informed consent collection in public health research projects of small or large scales while also being capable of complying with data governance requirements, healthcare standards, and ledger technologies. Key messages • Dynamic informed consent still lacks appropriate scrutiny over the underlining data structures utilized to store and manage dynamic consent. • Our work proposes a design in Unified Modelling Language (UML), of dynamic informed consent based on public health's domain mapping, data governance and public health research requirements.
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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 | 0.044 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".