A proposed approach for standardized reporting of data linkage processes and results.
Bibliographic record
Abstract
ObjectivesPopulation Data BC (PopData) is an agency in British Columbia, Canada, that routinely performs linkages of various administrative and researcher-collected data to a population spine. We developed a linkage report template in order to increase transparency of linkage process and outcome for end users and data providers. ApproachPopData performs probabilistic and deterministic data linkage using an in-house software. A literature review identified existing guidelines and examples of linkage reporting. A survey collected input from a wide range of end users about their interest in receiving linkage reports and specific information that is important to their work. A draft template was developed by PopData’s linkage experts and data scientists which then was reviewed by PopData staff and external partners. Privacy requirements, mode of delivery, readability to the intended audience and operational feasibility were carefully considered. ResultsThe resulting template built on our existing internal linkage summaries. The report follows a framework suggested in the literature with three key components: 1) information on the data source and linkage fields, 2) data pre-processing and linkage methodology, and 3) linkage results, presented in tables and figures, including overall linkage rates, detail on matched fields, and the distribution of linkage weights of linked and unliked pairs. In addition, an appendix describes the linkage methods and population spine in detail, and supplementary notes will comment on unique issues related to the data, when those are applicable. Educational materials to aid understanding of linkage methodologies and reporting are also under development. ConclusionLinked data are increasingly used in research, making it important to provide information on linkage process and performance to the research community. Rigorous and standardized linkage reports produced by data centres can facilitate evaluation of the impact of linkage performance on research findings and enable transparent reporting in peer-reviewed 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 | 0.398 | 0.497 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".