A Framework for the Protection of Privacy in an Electronic Health Environment
Bibliographic record
Abstract
This paper argues that given the proliferation of electronic health records (EHRs) in the health care system, legislative reform must occur to address the inadequacies of Ontario’s current health privacy legislation in accommodating EHRs. A coherent framework for legislation is necessary to capture the important role that privacy plays in public perception when it comes to legislating and managing EHRs in Ontario and, in turn, serve as a tool for legislators to understand the definitions and values of privacy associated with EHRs and the privacy problems worthy of protection in an electronic health environment. The failure to properly address these problems may lead to privacy losses and loss of public confidence in EHR systems. In applying this framework to three legislative options, it is evident that Ontario should amend the Personal Health Information Protection Act, 2004 to better contemplate the privacy protections necessary in an electronic health environment.
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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.059 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".