Privacy and the secondary use of data for health research: experience in Canada and suggested directions forward
Bibliographic record
Abstract
This paper has three parts. The first part describes developments in the use of personal information for health research in Canada, challenges in responding to new data protection legislation and the work of the Canadian Institutes of Health Research in assisting the research community to adapt to these developments. The second part critiques two aspects of the stakeholder analysis posited in William Lowrance's report: the lack of an analysis of researchers' interests as distinct from those of society at large, and the framing of privacy as an individual interest. I argue for recasting the privacy and research tension as a values conflict between two societal goods and not an individual versus a societal interest, and for explicitly acknowledging researchers' interests as data users. In so doing, there is no clear trump of privacy or research, and greater importance is placed upon the weighing of benefits and risks of individual studies to determine whether the waiving of consent for a particular study or programme of research is justified. The third part makes specific suggestions as to future ways in which data use practices may evolve. I argue the need to engage the public in deliberating the broad parameters under which personal information will be used, as the public collectively has a major stake in this issue and their input provides legitimacy for these uses of information. In addition, given the proliferation of prospectively-developed databases for research purposes, a 'systems' approach for obtaining consent for participation to replace the current study-by-study recruitment of individuals is appropriate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".