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Enregistrement W6964807279 · doi:10.25949/25939708.v1

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

2022· article· en· W6964807279 sur OpenAlexaboutno aff

Notice bibliographique

RevueINDIGO (University of Illinois at Chicago) · 2022
Typearticle
Langueen
DomaineHealth Professions
ThématiquePublic Health Policies and Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTransparency (behavior)Government (linguistics)Data Protection Act 1998LegislationFreedom of informationInformation privacyPersonally identifiable informationOpen governmentProcess (computing)Privacy law

Résumé

récupéré en direct d'OpenAlex

<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>

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,082
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0050,000
Communication savante0,0000,001
Science ouverte0,0010,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,265
Tête enseignante GPT0,485
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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