Discussing the Limits of Confidentiality: The Impact of Criminalizing HIV Nondisclosure on Public Health Nurses' Counseling Practices
Bibliographic record
Abstract
In Canada, there have been a growing number of criminal HIV nondisclosure cases where public health records have been subpoenaed to aid in police investigations and/or to be presented in court as evidence against HIV-positive persons. This has led some to suggest that nurses provide explicit warnings about the limits of confidentiality in relation to crimes related to HIV nondisclosure, while others maintain that a robust account of the limits of confidentiality will undermine the nurse–client relationship and the public health goals of reducing HIV/sexually transmitted infection transmission. This article engages with this issue by exploring whether and how public health nurses endeavor to control information about the limits of confidentiality at the outset of HIV posttest counseling. The data indicate variation in practices, as nurses pragmatically balance ethical and professional concerns; although some nurses intentionally withhold information about the risk of subpoena, others report talking to clients about confidentiality in ways that focus on the risk of harm associated with criminalization. The discussion argues that practice variation also illuminates medico-legal relations between health care and the criminal justice system. Data are drawn from qualitative interviews with 30 nurses working at four public health units in Ontario.
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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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".