Social networks for health care: Addressing regulatory gaps with privacy-by-design
Bibliographic record
Abstract
Social computing is a relatively new approach to systems design that emphasizes the importance of facilitating collaboration and communication between users. Although social networking is now part of mainstream culture, the use of these applications in the health care space is still in its infancy in Canada. As major vendors are preparing to enter the marketplace, it is important for a wide variety of stakeholders to discern the ramifications of this next wave of technological innovation. This paper discusses social networking applications for health care, and the challenges of dealing with this new type of information management system under current Canadian law. While regulatory authorities have considered the privacy and security implications of social networking in the course of investigating complaints, this paper contains the first explicit analysis of the legal difficulties surrounding the use of social networking for health care applications in Canada. Those risks not covered by the current regulatory framework are assessed from the standpoint of privacy-by-design, as we discuss how software developers can build privacy protection into social networking applications.
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 | 0.162 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.011 | 0.013 |
| 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".