Efficiency gains from the implementation of Research IDs for data linkage: A case study from Population Data BC and the Data Innovation Program in British Columbia, Canada
Bibliographic record
Abstract
Objective and ApproachPopulation Data BC (PopData) transitioned to Research IDs from Study IDs for the Data Innovation (DI) Program, with the objective of improving efficiency of data preparation, linkage, and provisioning while maintaining privacy standards. Study IDs are person-specific encrypted identifiers unique to each study, preventing Researchers from combining records from different projects, in turn reducing risk of re-identification. Research IDs are also person-specific encrypted identifiers, except they are not project specific. Advancements in technology and changes in data provisioning models have reduced utility of Study IDs. ResultsImplementing Research IDs instead of Study IDs decreased: processing, setup/review, per transfer effort, errors/omissions, storage requirements, staff training/knowledge, licenses, record keeping, specialized knowledge, and per project data tracking. There was a marginally higher risk of re-identification with Research IDs, counter-acted by a higher degree of technological security and stronger procedural and ethical frameworks, resulting in the ability to provide more data to more users. ConclusionsAdopting Research IDs has allowed PopData and the DI Program to access established data resources faster, more efficiently, with less per project person effort and reduced storage requirements without a net risk change. The benefits of adopting Research IDs, counter a potential minimal risk gain arising from replacement of Study IDs with Research IDs. ImplicationsEfficiency gains from the use of Research IDs allows PopData and the DI Program to redistribute operational and staff resources, in turn increasing their ability to scale, to provide data in more diverse formats and improve service provision.
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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 | 0.083 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".