Mapping the international ecosystem of national health data spaces. A scoping review protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.065 | 0.083 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it