O12-4 Data access and linkage, privacy and ethical concerns in epidemiological studies using administrative data: a canadian perspective
Bibliographic record
Abstract
Objectives Research organisations experience challenges accessing administrative and record level data due to legislative privacy restrictions and ethical considerations. Ensuring that individuals’ privacy are preserved while maintaining the utility of data, raises legal, ethical and privacy challenges for researchers conducting epidemiological studies. The Partnership for Work, Health and Safety (Partnership), an innovative Canadian research platform, must operate in a data-rich environment using administrative data such as occupation health data to provide evidence for policy-making that can improve workers health and safety. In order to have access to multijurisdictional data, it operates under an effective data access model that meets legislative, privacy and ethical concerns via our data partner, Population Data BC (PopData). Methods A flexible data access protocol has been developed accommodating a multi-legislative landscape. It is a centralised privacy and security model encompassing the Privacy by Design principles ensuring privacy controls and safeguards are in place to facilitate access to both linked and unlinked data and meet ethical concerns. These protocols meet ISO 27002 requirements for information security. Research data are housed in a Secure Research Environment (SRE) provided by PopData. The SRE is a central server accessible through a firewall only via an encrypted Virtual Private Network (VPN) using a SecurID token for authentication. The SRE provides the Partnership with secure storage and back up of data while generating audit log of all activities of the SRE. Researchers accessing data must complete privacy training and sign confidentiality undertakings. Results A model that offers a comparable level of data protection to all data providers and consistent data protection practices through a secure environment resulting in over 9 Data Sharing Agreements executed to access longitudinal population-based data. Conclusions Data access requires a rigorous “Privacy by Design” data access and infrastructure model and strong partnerships with stakeholders and data providers.
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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 | 0.074 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.027 | 0.034 |
| Scholarly communication | 0.032 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".