Hepatitis B virus infected physicians and disclosure of transmission risks to patients: A critical analysis
Bibliographic record
Abstract
BACKGROUND: The potential for transmission of blood-borne pathogens such as hepatitis B virus from infected healthcare workers to patients is an important and difficult issue facing healthcare policymakers internationally. Law and policy on the subject is still in its infancy, and subject to a great degree of uncertainty and controversy. Policymakers have made few recommendations regarding the specifics of practice restriction for health care workers who are hepatitis B seropositive. Generally, they have deferred this work to vaguely defined "expert panels" which will have the power to dictate the conditions under which infected health care workers may continue to practice. DISCUSSION: In this paper we use recent Canadian policy statements as a critical departure point to propose more specific recommendations regarding disclosure of transmission risks in a way that minimizes practice restriction of hepatitis B seropositive health care workers without compromising patient safety. The range of arguments proposed in the literature are critically examined from the perspective of ethical analysis. SUMMARY: A process for considering the ethical implications of the disclosure of the sero-status of health care workers is advanced that considers the varied perspectives of different stakeholders.
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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 | 0.107 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".