Bibliographic record
Abstract
The Ontario Population Health Index of Databases (OPHID) is an index of a wide variety of quantitative information sources for and about Ontario (Canada) that reflect both the state of the health of its populations and possible explanatory variables. OPHID is a rich information resource for health researchers. The collection represents a vast improvement for the availability of metadata for health data in Ontario (and Canada as whole), where health data are often disparately collected, poorly documented, and not available or known to the public. Researchers in population health and the health sciences increasingly require high-quality health data, especially as health research becomes more evidence-based and measure-driven. This comprehensive index of health data utilizes the Data Documentation Initiative (DDI-Codebook) standard to document and describe data of varying kinds. Data sources that are of a survey, clinical, and administrative nature are described using a core set of DDI fields, with some degree of difficulty arising around consistency across the kinds of data. This presentation will provide an overview of the OPHID project goals and objectives, while focusing on the technical implementation and process by which datasets are described and marked up using the DDI standard. OPHID is a joint collaboration among the Ontario Council of University Libraries, Scholars Portal, and the Population Health Improvement Research Network (PHIRN).
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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; 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".