IPDLN Workshop: Opportunities for Cross-Country Comparisons of Linked Administrative Education and Health Data
Bibliographic record
Abstract
ObjectivesTo understand how we can better initiate and run international collaborations to deliver tangible and impactful findings using population-wide linked health and education data. ApproachRepresentatives from 14 countries (America, Australia, Canada, Chile, Denmark, England, Finland, Germany, New Zealand, Northern Ireland, Norway, Scotland, Sweden, Wales) summarised the potential for research using their linked administrative health and education data. The scope for collaboration and comparison of research findings across countries was explored. ResultsSeveral substantive research themes emerged, including the quantifying of differences in educational outcomes by health conditions and other early life factors, using health data to better identify and explore the special educational needs identified in educational records, using linked health and education data to identify and explore reasons for absenteeism from school. ConclusionsThe workshop showcased the primary themes for research and the data assets available within each country for comparative work. Further work is required to more robustly document the available detail within these datasets, collaboratively develop protocol templates for comparative studies, and develop pilot, proof of concept, studies.
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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 | 0.388 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.008 | 0.041 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".