Social licence and the general public’s attitudes toward research based on linked administrative health data: a qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Both the research literature and headline news stories indicate that the public cares about how their health data are used. The objective of this study was to learn more about the general public's attitudes toward users and uses of linked administrative health data held by ICES in Ontario, Canada. METHODS: Eight focus groups, with a total of 65 members of the general public, were conducted in urban and northern settings in Ontario, Canada, in 2015 and 2017 using qualitative market research panels established by a market research/public opinion research firm. RESULTS: Three major themes emerged: (a) the need for assurance about privacy and security, (b) general support for research based on linked administrative health data with some conditions and (c) mixed and more negative reaction when there is private sector involvement. Two minor themes were also derived from the data: (a) low knowledge and understanding of how linked administrative health data are used for research and (b) mixed views on the need to obtain consent when health data do not include identifying information. INTERPRETATION: The public generally supports research based on linked administrative health data, but there is no blanket approval. Researchers and organizations that hold health data should engage with members of the public to understand and address their concerns about privacy and security and to ensure that research is aligned with social licence, particularly where there is private sector involvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it