A Metadata Manifesto: The Need for Global Health Metadata
Bibliographic record
Abstract
Administrative health data recorded for individual health episodes (such as births, deaths, physician visits, and hospital stays) are being widely used to study policy-relevant scientific questions about population health, health services, and quality of care. An increasing number of international health comparisons are undertaken with these data. An essential pre-requisite to such international comparative work is a detailed characterization of existing international health data resources, so that they can be more readily used for comparisons across counties. A major challenge to such international comparative work is the variability across countries in the extent, content, and validity of existing administrative data holdings. Recognizing this, we have undertaken an international proof of concept pilot compiling detailed data about data - i.e., a "meta-data catalogue" - for existing international administrative health data holdings. We describe the methodological process for collecting these meta-data, along with some general descriptive results for selected countries included in the pilot.
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.
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 | 0.206 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.027 | 0.065 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".