Challenges with organization, discoverability and access in Canadian open health data repositories
Bibliographic record
Abstract
Introduction: Open health data provides healthcare professionals, biomedical researchers and the general public with access to health data which has the potential to improve healthcare delivery and policy. The challenge is to create and implement appropriate metadata, or structured data about the data, to ensure that data are easy to discover, access and re-use. The goal of this study is to identify, evaluate and compare Canadian open health data repositories for their searching, browsing and navigation functionalities, the richness of their metadata description practices, and their metadata-based filtering mechanisms. Methods: Metadata-based search and browsing was evaluated in addition to the number and nature of metadata elements. Six Canadian open health data repositories across national, provincial and institutional levels were evaluated. Data collected using verbatim text recording was evaluated using an analytical framework based on the 2019 Dataverse North Metadata Best Practices guide and 2019 Data Citation Implementation Project roadmap. Results: All repositories required filtering to access "open health data." All repositories included 'subject' facets for filtering, and 'title' and 'description' on the Results List. Use case evaluations suggest improvements including advanced search, health-specific search terms, records for all repositories, and links to related publications. Discussion: Consistent use of 'title' and 'description' suggests that an interoperable interface is possible. Inconsistencies in records indicate the need for explicit, easy to find mechanisms to access metadata in repositories. The analytical framework represents first draft guidelines for metadata creation and implementation to improve organization, discoverability, and access to Canadian open health data.
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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 | 0.153 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.033 | 0.058 |
| Science and technology studies | 0.031 | 0.021 |
| Scholarly communication | 0.051 | 0.022 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".