Mapping the international ecosystem of national health data spaces. A scoping review protocol
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> The reuse of participant-level health data by public health and surveillance institutions, hospitals, doctors, and patients is an emerging priority for a number of national governments. Technical and semantic interoperability of health data ecosystems is important for detecting and responding to global health challenges, including emerging infectious diseases, antimicrobial resistance, and vaccine-preventable illnesses. In this scoping review, we will identify and describe health data ecosystems, spaces, clouds, and commons, national-level mechanisms for enabling the reuse of participant-level health data. </ns3:p> <ns3:p> <ns3:bold>Methods and analysis:</ns3:bold> We will apply the Arskey and O’Malley scoping review approach to describe governance, content, and semantic and technical interoperability of data and metadata in national health data ecosystems. We selected a scoping rather than a systematic review methodology to provide a high-level analysis of the current state of health data ecosystems’ implementation of the FAIR principles for data resources. The systematic search strategy was pilot tested and tailored for Ovid(Medline), CINAHL, and Web of Science. We will also conduct web scraping and consult stakeholders to identify additional health data ecosystems. Two reviewers will conduct the title-abstract and full-text screening and data charting independently. Discrepancies will be resolved by consensus, and results will be summarized in narrative form. </ns3:p> <ns3:p> <ns3:bold>Ethics and dissemination:</ns3:bold> Ethical approval is not required for this scoping review of published studies and grey literature. The scoping review protocol was registered prior to initiating the search strategy. Study results will be submitted for publication in an Open Access journal. </ns3:p>
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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 | 0.187 | 0.214 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.084 | 0.020 |
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".