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Record W2301813002 · doi:10.1080/17441692.2016.1146318

‘ … They should understand why … ' The knowledge, attitudes and impact of the HIV criminalisation law on a sample of HIV+ women living in Ontario

2016· article· en· W2301813002 on OpenAlexafffundabout
Lydia Kapiriri, Wangari Tharao, Marvelous Muchenje, Khatundi Masinde, Fanta Ongoïba

Bibliographic record

VenueGlobal Public Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsWomen's Health In Women's HandsWomen's College HospitalMcMaster University
FundersCenters for Disease Control and PreventionOntario HIV Treatment Network
KeywordsHuman immunodeficiency virus (HIV)Sample (material)CriminologyLawPolitical sciencePsychologySociologyMedicineFamily medicine

Abstract

fetched live from OpenAlex

Over 60 countries criminalise 'the "willful" transmission of HIV'. Such a law has the potential to hinder public health interventions. There is limited literature discussing the perceptions of this law and the impact, it has had on HIV-positive women. This paper describes the knowledge of and attitudes of this law by HIV-positive women living in Ontario; and their experiences with its application. Three group discussions (n = 10) and 17 in-depth interviews with HIV-positive women age: 21-56 years. Data were analysed using a modified thematic approach. Most of the respondents knew about the law with regard to adult HIV transmission. However, very few knew about any laws related to mother to child HIV transmission, although some reported having had their children taken away because of breastfeeding. Respondents felt that the law could be fair and protective if there were means of providing a priori support to those women who have been disadvantaged social-culturally and structurally. Without this support, the law could potentially lead HIV-positive women into hiding and not accessing services that could help them. There is need for the law implementers to consider these findings if they are to support the public health efforts to control HIV.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.373
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations13
Published2016
Admission routes3
Has abstractyes

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