Thierry Vansweevelt and Nicola Glover-Thomas (eds.), <i>Privacy and Medical Confidentiality in Healthcare: A Comparative Analysis</i>
Bibliographic record
Abstract
As every medical law student, scholar, and practitioner knows, a core component of the subject area is confidentiality (sometimes described more narrowly as ‘medical confidentiality’). By this is meant the duty—moral, professional, and legal—that a healthcare professional owes to a patient or healthcare service user to hold in confidence the personal information that the patient or service user has shared with them via oral or written communication. The professional, duty-bound (and conscience-bound), must hold that information secret. Upholding this duty, which can be traced back at least to the Hippocratic Oath of Ancient Greece, is fundamental to establishing and maintaining the bond of trust between the practitioner and patient, and between all of society and the healthcare system more widely. This much we know and consider ‘sacred’ in the Talmud of our subject area. But contemporary developments in medicine, including increased reliance on telemedicine (pre- and post-dating the coronavirus disease 2019 pandemic) and the rise of the patient autonomy movement, as well as wider developments in the law (including data protection reform and the proposed regulation of artificial intelligence) generate new questions about the ‘state of play’ in this area. Alongside this, in recent years, the study and teaching of ‘the protection of patient information’ has expanded beyond medical confidentiality to also consider the role of privacy (as a concept and legal discipline) and data protection (as a concept and legal discipline) as the latter two also now play a fundamental role in the doctor–patient relationship—and in ways that differ from the long-standing role confidentiality has played.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".