Criminalization of HIV non-disclosure: Narratives from young men living in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Previous research has identified the impacts of legal frameworks that criminalize HIV non-disclosure among people living with HIV (e.g., elevated stigma and violence). However, far less is known about the perspectives or experiences of people-particularly, men-who are HIV-seronegative or who are unaware of their status. The objective of this paper is to describe the health and social risks that young men perceive to be associated with an HIV diagnosis in the context of Canada's current legal framework pertaining to HIV non-disclosure. METHODS: We analyzed data from 100 in-depth interviews (2013-2016) conducted with 85 young men ages 18-30 in Vancouver on the topic of the criminalization of HIV non-disclosure. RESULTS: Our analysis revealed two dominant narratives in relation to HIV criminalization: (a) interrogation and (b) justification. An interrogation narrative problematized the moral permissibility of criminalizing HIV non-disclosure. In this narrative, Canada's HIV non-disclosure legal framework was characterized as creating unjust barriers to HIV testing uptake, as well as impeding access to and reducing retention in care for those living with HIV. Conversely, a justification narrative featured a surprising number of references to HIV as a "death sentence", despite effective treatments being universally available in Canada. However, most of those who presented the justification narrative asserted that the criminalization of HIV non-disclosure was morally justified in light of the perceived negative stigma-related impacts of HIV (e.g., discrimination; being ostracized from sex or romantic partners, friends, family). The justification narrative often reflected a belief that the legal framework provides both punishment and deterrence, which were perceived to supersede any barriers to care for both HIV-positive and -negative individuals. CONCLUSION: Public education regarding contemporary medical advances in HIV may help contest lay understandings of HIV as a "death sentence", which is particularly relevant to destabilizing justification narratives. However, significant strengthening of HIV stigma-reduction efforts will be needed to move society away from narratives that attempt to justify Canada's current HIV non-disclosure legal framework.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".