Understanding health data social licence: An international comparison of community attitudes towards health data use across Canada and Australia
Bibliographic record
Abstract
ObjectiveResearch has found general but conditional support for health data being used for public benefit. The term “social licence” describes which uses of health data the public supports and under what conditions. Here, we aim to compare two approaches to understanding community attitudes towards health data use, and how social licence may differ, between Canadian and Australian populations. ApproachFactors that affect community support for health data use include the specific population, the type(s) of data, and the engagement approach used. In Canada, facilitated dialogues were held to explore whether (i) there were uses of health data that diverse members of the public all supported and (ii) there was consensus on essential requirements for health data social licence. In Australia, national surveys and citizens’ juries were conducted to better understand (i) attitudes towards private sector data use and (ii) the ethical, legal and social implications of using general practice data in research. ResultsDespite the different approaches taken, many conditions for social licence were similar across Canadian and Australian participants. Both groups agreed on conditions for health data social licence related to equity, governance, privacy and transparency. However, there was a stark contrast between levels of support for private sector data use, personal control and consent. ConclusionThis comparative exercise contributes valuable insights into the ongoing dialogue surrounding community attitudes towards health data use. Continued research monitoring health data social licence across populations is imperative for public trust while gaining full benefits from health data use in research.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".