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Record W6964807279 · doi:10.25949/25939708.v1

Access to government data collections of personal information for health research: better decision making

2022· article· en· W6964807279 on OpenAlexaboutno aff

Bibliographic record

VenueINDIGO (University of Illinois at Chicago) · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)Government (linguistics)Data Protection Act 1998LegislationFreedom of informationInformation privacyPersonally identifiable informationOpen governmentProcess (computing)Privacy law

Abstract

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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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.307
metaresearch head score (Gemma)0.463
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3070.463
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.013
Science and technology studies0.0130.035
Scholarly communication0.0620.057
Open science0.0080.038
Research integrity0.0160.030
Insufficient payload (model declined to judge)0.0290.016

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.

Opus teacher head0.265
GPT teacher head0.485
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueINDIGO (University of Illinois at Chicago)Same topicPublic Health Policies and EducationFrench-language works237,207