Trust, trustworthiness and sharing patient data for research
Bibliographic record
Abstract
When it comes to using patient data from the National Health Service (NHS) for research, we are often told that it is a matter of trust: we need to trust, we need to build trust, we need to restore trust. Various policy papers and reports articulate and develop these ideas and make very important contributions to public dialogue on the trustworthiness of our research institutions. But these documents and policies are apparently constructed with little sustained reflection on the nature of trust and trustworthiness, and therefore are missing important features that matter for how we manage concerns related to trust. We suggest that what we mean by 'trust' and 'trustworthiness' matters and should affect the policies and guidance that govern data sharing in the NHS. We offer a number of initial, general reflections on the way in which some of these features might affect our approach to principles, policies and strategies that are related to sharing patient data for research. This paper is the outcome of a 'public ethics' coproduction activity which involved members of the public and two academic ethicists. Our task was to consider collectively the accounts of trust developed by philosophers as they applied in the context of the NHS and to coproduce an argumentative position relevant to this context.
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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 | 0.388 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.159 |
| Scholarly communication | 0.030 | 0.045 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".