Medical privacy and liability for its violation: The experience of the U.S. and Canada
Bibliographic record
Abstract
The aim of this research paper is to conduct a comparative legal study of medical privacy protection based on the regulatory practices of the United States and Canada. The research relevance stems from the growing role of information in the modern world, driven by the rapid development of information and communication technologies (ICT) and the emergence of new challenges related to unauthorized access to personal data. Medical information is treated as sensitive data requiring special protection due to its direct and intimate connection to specific individuals. This explains the specialized regulations in the US and Canada to safeguard medical privacy, backed by liability measures. The study employs the following methods: system analysis, which examines medical privacy protection systems holistically in each country; comparative legal analysis, which juxtaposes specific regulatory aspects of liability for medical privacy violations; analogy, which identifies similarities and parallels across jurisdictions; induction, which extracts common features from particular provisions; and retrospective analysis, which traces the historical evolution and refinements in medical privacy protection. The research yields several conclusions regarding both countries and assesses development trends in medical privacy protection in the US and Canada.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.047 | 0.017 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".