Involving the Public in Data Linkage Research
Bibliographic record
Abstract
Introduction“The Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research”, recent data breaches, and growing public awareness and controversy associated with secondary use of health data all highlight the need to understand what data sharing the public will support, under what circumstances, for what purposes and with whom. Objectives and ApproachThis symposium explores methods and findings from public engagement at all stages of data linkage research, beginning with short presentations (~6-8 minutes) on recent work: Mhairi Aitken: Consensus Statement - principles and an application using deliberative workshops to explore public expectations of public benefits from data-intensive health research Annette Braunack-Mayer/Felicity Flack: Surveys and citizens’ juries: Sharing government data with private industry Kim McGrail/Mike Burgess: Public deliberations on cross-sector data linkage, and combining public and private sources of data Alison Paprica: Plain language communication informed by Health Data Research Network Canada’s Public Advisory Council. Half the session will be spent interacting with the audience through live polling. The moderator will post a series of poll question such as “What is the most important thing for meaningful public engagement?” to prompt audience thinking on the topic. After the audience responses are revealed, panelists will share their own views about what they think is the best answer, and the main reason(s) behind their choice. The last 10-15 minutes of the session will be reserved for Q&A and dialogue with the audience. ResultsWe anticipate that this approach will surface emerging and tacit knowledge from presenters and the audience, and augment that through generative discussion. Conclusion / ImplicationsSession attendees will leave with a better understanding of the current state of knowledge and ways to talk about that understanding with other researchers, policy makers and the public.
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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 | 0.325 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier 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".