Administrative Record Linkage as a Tool for Public Health Research
Bibliographic record
Abstract
Linked administrative databases offer a powerful resource for studying important public health issues. Methods developed and implemented in several jurisdictions across the globe have achieved high-quality linkages for conducting health and social research without compromising confidentiality. Key data available for linkage include health services utilization, population registries, place of residence, family ties, educational outcomes, and use of social services. Linking events for large populations of individuals across disparate sources and over time permits a range of research possibilities, including the capacity to study low-prevalence exposure-disease associations, multiple outcome domains within the same cohort of individuals, service utilization and chronic disease patterns, and life course and transgenerational transmission of health. Limited information on variables such as individual-level socioeconomic status (SES) and social supports is outweighed by strengths that include comprehensive follow-up, continuous data collection, objective measures, and relatively low expense. Ever advancing methodologies and data holdings guarantee that research using linked administrative databases will make increasingly important contributions to public health 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 | 0.226 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.036 | 0.069 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".