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Should Caregivers Be Compelled to Disclose Patients' HIV Infection to the Patients' Sex Partners Without Consent?

2007· article· en· W2097180246 on OpenAlexaboutno aff
Babafemi Odunsi

Bibliographic record

VenueStudies in Family Planning · 2007
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsnot available
FundersBritish Medical Association
KeywordsSerostatusConfidentialityDilemmaMedicinePandemicDuty to warnDutyDiscretionDenialPublic healthDuty to protectFamily medicineHuman immunodeficiency virus (HIV)Political sciencePsychologyLawNursingCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

The emergence of the HIV/AIDS pandemic has added to the tension between patients' private interests and public health interests regarding medical confidentiality. Many people become infected with HIV because they are unaware of the positive serostatus of their sexual partners. Informing or warning the sexual partners of HIV-positive patients of the patients'serostatus could assist in curtailing the spread of HIV/AIDS because sexual partners can thereby choose to avoid having unprotected sex with infected persons. By law, however, doctors have a duty to their patients to protect their medical confidentiality. Doctors, therefore, face a dilemma concerning which should prevail: patients' right to privacy and confidentiality or the importance to society of controlling the spread of the pandemic. Most medical regulatory bodies do not take clear-cut positions on the issue, leaving the decision to the discretion of individual doctors. The question of whether doctors should be legally empowered to breach the confidence of patients to protect the patients' sexual partners is discussed here with reference to the existing laws of Canada, the United States, and Nigeria.

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 imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.136
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.010
Scholarly communication0.0040.008
Open science0.0010.003
Research integrity0.0140.010
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.233
GPT teacher head0.435
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations12
Published2007
Admission routes1
Has abstractyes

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