Health data social licence: An inclusive process to learn more about the perspectives of experienced public and patient advisors
Bibliographic record
Abstract
The term "social licence" has been used to describe which uses and users of health data the public supports - and under what conditions. From November 2022 to January 2023, Health Data Research Network Canada was funded by the Public Health Agency of Canada to explore whether there was consensus among experienced public and patient advisors on: (i) uses of health data that all members supported or opposed and (ii) what constitutes an essential requirement for a health data use or user to be within social licence. The project was conducted in English and French in collaboration with the Interdisciplinary Research Group in Health Informatics (GRIIS) at the University of Sherbrooke. It involved 20 public/patient advisor "participants" and an additional 13 public/patient advisors who served as peer-reviewers, all of whom had prior experience working in a health-related field and/or with health data. The process followed inclusive design principles in that it captured views held by the majority and minority of participants, including views expressed by only one or two participants. After two 2-hour facilitated sessions, participants agreed that it is within social licence for health data to be used (i) by healthcare practitioners to improve patient care, (ii) by governments and administrators to improve the health system, and (iii) by university-based researchers to understand disease and well-being. There was consensus opposition to (i) an individual or organisation selling someone else's identified health data and (ii) health data being used for a purpose that has no public or societal benefit. There was no consensus about what constitutes an essential requirement for a use or users of health data to be with social licence. The results of the process have been published in a non-peer-reviewed report co-authored with participants. This paper has been co-authored with a subset of the participants and peer-reviewers to present a high-level summary of the findings, methodological details, and templates to enable other groups to adapt the process to their own settings. It also presents the results of an anonymous evaluation of the process using the Public and Patient Engagement Evaluation Tool (PPEET), which were mostly positive and identified some areas for improvement.
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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 | 0.371 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.043 | 0.039 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.007 | 0.072 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".