Administrative Record Linkage as a Tool for Public Health Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it