New Federal and Provincial Personal Information Protection Legislation and its Impact on Physicians and Public Hospitals
Bibliographic record
Abstract
The focus of this article is to examine the implications of the new federal and Ontario personal data protection legislation for physicians and public hospitals. This article also inquires into whether the new legislation will contribute to the protection of patient privacy. By ‘‘physician’’ I mean a doctor in a broad sense – i.e., ‘‘a person who has been educated, trained, and licensed to practice the art and science of medicine’’. This will include family doctors, paediatricians, psychiatrists, surgeons, and other medical doctors covered by the Regulated Health Professions Act. By the term ‘‘public hospitals’’ I will refer to not-for-profit hospitals as they are defined by the Public Hospitals Act.\nThe first part of the article will examine the interre- lationship between the personal data protection legisla- tion and existing standards for physicians. The second part will analyze PIPEDA and its implications for doctors and public hospitals. Lastly, I will analyze the new Ontario legislation designed to protect personal information in the context of health care and treatment.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".