Research, Digital Health Information and Promises of Privacy: Revisiting the Issue of Consent
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
The obligation to maintain the privacy of patients and research participants is foundational to biomedical research. But there is growing concern about the challenges of keeping participant information private and confidential. A number of recent studies have highlighted how emerging computational strategies can be used to identify or reidentify individuals in health data repositories managed by public or private institutions. Some commentators have suggested the entire concept of privacy and anonymity is “dead”, and this raises legal and ethical questions about the consent process and safeguards relating to health privacy. Members of the public and research participants value privacy highly, and inability to ensure it could affect participation. Canadian common law and legislation require a full and comprehensive disclosure of risks during informed consent, including anything a reasonable person in the participant or patient’s position would want to know. Research ethics policies require similar disclosures, as well as full descriptions of privacy related risks and mitigation strategies at the time of consent. In addition, the right to withdraw from research gives rise to a need for ongoing consent, and material information about changes in privacy risk must be disclosed. Given the research ethics concept of “non-identifiability” is increasingly questionable, policies based around it may be rendered untenable. Indeed, the potential inability to ensure anonymity could have significant ramifications for the research enterprise.
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 | 0.009 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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