Privacy and security in the era of digital health: what should translational researchers know and do about it?
Bibliographic record
Abstract
The rapid growth in the availability and incorporation of digital technologies in almost every aspect of our lives creates extraordinary opportunities but brings with it unique challenges. This is especially true for the translational researcher, whose work has been markedly enhanced through the capabilities of big data aggregation and analytics, wireless sensors, online study enrollment, mobile engagement, and much more. At the same time each of these tools brings distinctive security and privacy issues that most translational researchers are inadequately prepared to deal with despite accepting overall responsibility for them. For the researcher, the solution for addressing these challenges is both simple and complex. Cyber-situational awareness is no longer a luxury-it is fundamental in combating both the elite and highly organized adversaries on the Internet as well as taking proactive steps to avoid a careless turn down the wrong digital dark alley. The researcher, now responsible for elements that may/may not be beyond his or her direct control, needs an additional level of cyber literacy to understand the responsibilities imposed on them as data owner. Responsibility lies with knowing what you can do about the things you can control and those you can't. The objective of this paper is to describe the data privacy and security concerns that translational researchers need to be aware of, and discuss the tools and techniques available to them to help minimize that risk.
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 | 0.155 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".