PSST… privacy, safety, security, and trust in health information websites
Bibliographic record
Abstract
Various newsworthy incidents typically include breaches of security, invasion of privacy, and harm caused by false information. In the e-health domain, there has been a lot of focus on ethical issues when dealing with electronic health records (EHRs) and patient medical records (PMRs). However, equally important are the myriad of health information websites that are being used to formally or informally get medical advice online. This study surveys related work on three popular and pertinent issues in health information websites: privacy, security, and trust. Our contributions include a succinct survey of different categories of popular health information websites (WebMD.com, MayoClinic.com, KidsHealth.org, PatientsLikeMe. com) to gauge existing methods for handling these issues. Moreover, an agenda is proposed for understanding the three issues orthogonally via access control. Other outcomes of the study include recommendations for open problems identified in health websites, including the need for fine-grained privacy, security and trust controls.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".