Research data use in a digital society: a deliberative public engagement
Bibliographic record
Abstract
Background: Sources of public and private data and ways to link them continue to evolve. This offers new opportunities for research, and new reasons for data-holding organisations to form partnerships. While research using these data can be beneficial, there is also a potential for negative consequences for some individuals or groups, including unintended or unanticipated effects. It is important to consult the public on how we might achieve both opportunities to link different types of data for research purposes, and protections against the misuse of data and the possibility of negative consequences. Methods: Combining data sources for research was the topic of four days of deliberation held in British Columbia, Canada in late 2019. Public deliberation events bring diverse groups of people together to give direct input to policy makers, through carefully structured in-depth discussion on issues that are controversial and/or a source of public concern. Participants discussed whether data from electronic medical records should be used for research purposes, whether it is acceptable to combine data from public and private sources, who should authorise its use in research, and how a public advisory group on data use might be structured. Results: Over four days, 29 residents of BC developed 17 deliberative conclusions that can be grouped into four broad topic areas: balancing benefit and potential harms when linking data; the protections that are expected to govern use of data; the type of authorisation required; and how the public should be involved in an ongoing way. Overall, the public is very supportive of research as long as oversight and controls are in place, including ongoing input from members of the public. Conclusion: Deliberative conclusions from this event provide essential public input on the use of linked data for research, in particular when those data come from multiple sources. This is important information as policy-makers continue to develop legislation and practices around the use and linkage of both public and private sources of data.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".