A Comparative Analysis of the Legal and Bioethical Frameworks Governing the Secondary Use of Data for Research Purposes
Bibliographic record
Abstract
The secondary use of research and health data for purposes that differ from the original purpose of the collection is becoming a major trend in research, since it allows for the optimal use of already available resources, and reduces the costs of research activities. However, the consent provided at the time of the initial data collection might not have foreseen these new uses of the data. This is especially true for biobanks having collected data under a restricted or a disease-specific consent, and for data linkage, which allows researchers to combine research data with information from the medical record of participants. To protect the participants' privacy, confidentiality, and autonomy, the use of identifiable research and clinical data for secondary research purposes is governed by a rather complex legal and ethical framework. This article aims to: (1) provide a comprehensive analysis of the legal and bioethical framework governing the secondary use of data at the international level, and; (2) identify points of convergence and divergence with regard to the secondary use of data for research purposes, in five countries (Australia, Canada, France, United Kingdom, and United States). While the secondary use of already collected data carries benefits and drawbacks, the international and national legal framework provide guidance to promote a wider (although limited) secondary use of data, while protecting research participants' rights and interests. Despite some differences, the similarities between international and national regulations and norms reveal the emergence of a common set of criteria for the secondary use of data in international research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".