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Record W2593322890

Personal Medical Information: Privacy or Personal Data Protection?

2006· article· en· W2593322890 on OpenAlexaboutno aff
Wilhelm Peekhaus

Bibliographic record

VenueeYLS (Yale Law School) · 2006
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsnot available
Fundersnot available
KeywordsPersonally identifiable informationInternet privacyBusinessPrivacy protectionInformation privacyMedical informationComputer securityComputer scienceInformation retrieval
DOInot available

Abstract

fetched live from OpenAlex

Some of the existing literature concerning the privacy of health information seems to suggest that medical information has a particularly special nature; either through its oft-cited association with dignity or the need for its ‘‘unobstructed’’ use by health care practitioners for a variety of reasons. It is against such a backdrop that this paper will review and compare a number of legislative mechanisms that have been designed to meet the challenge of safeguarding the privacy of personal information without completely hindering the continued flow of information required by economic and health care systems. An attempt will be made to situate the Canadian legal environment in respect of privacy legislation within a suitable theoretical framework: Elizabeth Neill’s model of privacy. Aside from providing the necessary conceptual framework for the paper that will help delineate between privacy and personal data protection, Neill’s model will be adapted to develop a privacy–personal data protection continuum, on which the various legislative devices will be positioned. The analysis of the various statutory mechanisms will be limited to a descriptive discussion designed to conceptualize the degree to which contemporary legislation is more aptly construed as protective of privacy or personal data. Though some attention will be devoted to discussing the analytic advantages of Neill’s model in responding to such a query, a normative assessment of her model or the various acts is beyond the scope of this paper. The research questions driving this paper include the fol- lowing three:\n(i) Considering Neill’s ontology of privacy rights, are the Organization for Economic Co-operation and Development’s Guidelines on the Protection of Privacy and Transborder Flows of Personal Data and the European Union Directive on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data best characterized as protective of privacy or personal data?\n(ii) Do the various provincial health information protection Acts go beyond the Personal Information and Protection of Electronic Documents Act such that health information protection might better be considered more about privacy than personal data protection?\n(iii) Which are aligned with Neill’s model?\nIn order to respond to these questions, the first part of the essay will be devoted to explicating Neill’s ontology of privacy. The paper will then consider the Organization for Economic Co-operation and Development’s Guidelines on the Protection of Privacy and Transborder Flows of Personal Data and the European Union Directive on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data in order to assess whether they protect privacy or personal data. The next section will engage in a comparative examination of the Canadian federal Personal Information and Protection of Electronic Documents Act and the four provincial health information protection Acts (Alberta, Saskatchewan, Manitoba, and Ontario). Based upon this comparison, attention will then turn toward an assessment of whether the various statutes are concerned more with privacy or personal data protection, and where they fit in the privacy debate based on Neill’s model.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.055
GPT teacher head0.300
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2006
Admission routes1
Has abstractyes

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