‘ … They should understand why … ' The knowledge, attitudes and impact of the HIV criminalisation law on a sample of HIV+ women living in Ontario
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".