Data sharing in multi-site, multi-country trials
Bibliographic record
Abstract
Context: Primary health care (PHC) researchers increasingly need to share their data. This requires adherence to privacy laws, approval by ethics committees and comprehensive data management plans. IMPACT is a 6-site Canadian-Australian collaborative research program designed to improve access to primary health care for vulnerable individuals. Each site used a common protocol to design and evaluate its own intervention, with the aim of pooling data across the sites. Ethics applications were submitted in each site. Sharing data across the sites proved a challenge. Objective: This paper describes the data-sharing issues managed by IMPACT. Design: Consultations were conducted with key informants and documents were reviewed to develop a data sharing plan. Key informants had expertise in data governance, IT and/or research ethics in medical research. Documents included universities policies and submissions to ethics committees. The authors reflected upon the process and identified lessons for PHC researchers. Setting: IMPACT study sites in 3 Australian jurisdictions (New South Wales, Victoria, South Australia) and 3 Canadian jurisdictions (Alberta, Quebec, Ontario). Results: Data sharing for a multi-site multi-country study was complex. Privacy legislation and university policies were protective of patient privacy, particularly in Australia. The site-specific ethics applications had not specified that the data was part of a larger study. Consequently, the other sites were deemed as external. Multiple consultations with ethics, IT, and data governance units were required to clarify the data classification (patient data is inherently sensitive), who needed access, and how access could be enabled. Bringing support units together assisted a common understanding – this had not been usual practice. Conclusions: Early consultations with university ethics and data governance units is recommended for planning data sharing – particularly for patient data and complex projects.
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How this classification was reachedexpand
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 | 0.022 |
| 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 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".