Bibliographic record
Abstract
The advent of open data in health care has increased healthcare innovation, with the publication of complete datasets aggregated by private and public entities that lead to efficiency through crowdsourcing working code, facilitating research into personalized medicine, and publishing reproducible data pipelines for experimental validation. However, there lacks an internationally recognized definition for health data governance at the scope of individual health data and open source big data, which bring about a discussion about the implications of open data on data privacy. First, healthcare data sourced directly from public healthcare systems: by whom and for what purpose is these data used for within the context of healthcare research. Second, health data from private research: the regulations needed for mutual disclosure. Third, personal user-generated health data: safeguards in a digital era needed to prevent misappropriation and abuse. This paper addresses the opportunities of open data in healthcare research in a digital age without transparent regulation. The consequence of open data on privacy leads to a framework of four safeguards for stakeholders: public education, operational transparency, regulation for accountability, and validation of research ethics. It also pioneers public policy direction for a balanced agenda between privacy and healthcare research.
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 | 0.038 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".