In Women's Eyes: Key Barriers to Women's Access to HIV Treatment and a Rights-Based Approach to their Sustained Well-Being.
Bibliographic record
Abstract
There is rightly a huge global effort to enable women living with HIV to have long productive lives, through treatment access. However, many women living with HIV experience violence against women (VAW), in both domestic and health care settings. The ways in which VAW might prevent treatment access and adherence for women has not to date been reviewed coherently at the global level, from women's own perspectives. Meanwhile, funding for global health care, including HIV treatment, is shrinking. To optimize women's health and know how best to optimize facilitators and minimize barriers to access and adherence, especially in this shrinking funding context, we need to understand more about these issues from women's own perspectives. In response, we conducted a three-phase review: (1) a literature review (phase one); (2) focus group discussions and interviews with nearly 200 women living with HIV from 17 countries (phase two); and (3) three country case studies (phase three). The results presented here are based predominantly on women's own experiences and are coherent across all three phases. Recommendations are proposed regarding laws, policies, and programs which are rights-based, gendered, and embrace diversity, to maximize women's voluntary, informed, confidential, and safe access to and adherence to medication, and optimize their long-term sexual and reproductive health.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".