Access to government data collections of personal information for health research: better decision making
Bibliographic record
Abstract
<p dir="ltr">Governments hold vast collections of personal information about citizens and residents, including information collected during the provision of health services. This data is generally collected for administrative purposes rather than for the primary purpose of research. These data collections are, however, potentially a rich resource for researchers. Such information is particularly powerful in the field of public health research — which looks at factors that determine the health of whole populations — and to support evidence-based public health policy development. The decision to release data to researchers for a particular project is taken by government data custodians, or data stewards, who have legal responsibility for administering their data. There has been criticism from researchers that data custodian decision-making processes lack transparency and lead to denials and delays that derail important research that is in the public interest. The primary focus of this thesis is a consideration of the decision-making process undertaken by Australian government data custodians. This process is heavily regulated, including by data protection law, duties of confidentiality, and legislation authorising collection and release of data. The thesis investigates how this regulatory framework, and the process of decision making within the regulatory framework, might be improved to bring them more into line with the values underpinning open government and good administrative decision-making: transparency, consistency, and accountability. The thesis also examines the extent to which relevant human rights are reflected in the regulatory arrangements in Australia, Canada and the United Kingdom concluding that, while the right to privacy is expressly articulated and given emphasis in these arrangements, the right to health and the right to enjoy the benefits of scientific progress are not appropriately represented. This is a thesis by publication including nine articles, submissions, and book chapters. These publications consider the regulatory framework and the decision-making process from a range of perspectives and employ a range of methodologies including doctrinal, comparative, empirical and law reform paradigms. The qualitative research conducted as part of this project documents for the first time the views and experiences of data custodians across Australia. The thesis employs doctrinal and comparative legal research to analyse and critique the regulatory arrangements in three jurisdictions — Australia, Canada and the United Kingdom — purposively chosen on the basis that they are global leaders in research using linked data with developed regulatory arrangements and shared common law foundations. In addition, all three jurisdictions have some form of constitutional power sharing arrangements in place, which impact on the legal and policy arrangements for sharing data, including across jurisdictional boundaries. The thesis proposes a range of changes to regulatory and administrative decision-making arrangements in Australia to ensure that data custodians make decisions that reflect open government values and are consistent with the principles of good administrative decision making. The thesis also makes recommendations that aim to ensure that the regulatory framework more broadly respects and protects the full range of relevant human rights. While the focus of these recommendations for change is Australian law and practice, there commendations are likely to be more broadly applicable.</p>
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".