Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.068 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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 source (direct Gemma or distilled Codex), 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".