When may existing personal information and biospecimens be used for health research and planning? Themes from a series of deliberative dialogues
Bibliographic record
Abstract
Issue Existing data and biosamples are increasingly important sources for health research and planning. In public health many of these uses are either permitted or mandated in law. However, some conditions remain contentious. Description We conducted a series of deliberative dialogues with the public, researchers, data custodians, regulators and privacy advocates on the conditions under which personal information and biosamples may be used for health research and planning. Dialogues were carried out over two years, led by an independent moderator and the PI. Round 1 dialogue involved researchers, custodians, regulators, and privacy advocates. Round 2 involved the general public. Participants received preparatory reading. Discussions focused on 3 case studies: secondary use of blood samples; linking health and non-health databases; and an academic post-marketing drug surveillance network. Findings from both rounds were presented to policy and lawmakers from across Canada for their insights and recommendations. Results The public were more trusting in government and academic users than were Round 1 participants. All deliberations revealed mixed feelings over commercialization of analytics and other research products. Trust was tied to governance mechanisms. Established data institutes were better trusted to have the conditions for secure management of data. The public did not wish to impede research but did wish to retain some control over use of their data, even if only the ability to opt out of some uses. There was not consensus among policy makers on this. All parties endorsed greater transparency with the public over “secondary” uses of the data but infrastructures to permit public engagement need to be in place first. The public were very concerned over potential adverse social consequences of the record-linkage study, including stigmatization of individuals, communities, and neighbourhoods. Lessons There is public support for these research activities, but concerns exist over commercialization, quality of governance of research uses, and greater transparency. Key messages Good governance processes are key. Greater public transparency is needed about research uses of data. Infrastructures to permit public engagement with the system need to be in place first.
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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 | 0.251 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.062 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.024 | 0.050 |
| Insufficient payload (model declined to judge) | 0.002 | 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".