Barriers and facilitators of HIV partner status notification in low- and lower-middle-income countries: A mixed-methods systematic review
Bibliographic record
Abstract
BACKGROUND: The uptake of HIV partner status notification remains limited in low- and lower-middle-income countries. This mixed-methods systematic review aims to summarize the barriers and facilitators of HIV partner status notification in these settings. METHODS: We searched PubMed, Embase, CINAHL, PsychINFO, Scopus, and Web of Science from January 01, 2000, to August 31, 2023, for empirical qualitative and quantitative studies. Two independent reviewers completed the title, abstract, full-text screening, and data extraction. The risk of bias was assessed using a mixed-methods appraisal tool (MMAT), and the study findings were summarized narratively. RESULTS: Out of the 2094 studies identified, 59 relevant studies were included. Common barriers included fear of stigma and discrimination, violence, abandonment, breach of confidentiality and trust, low HIV-risk perception, and limited knowledge of HIV and HIV testing. Facilitators of HIV partner status notification were feelings of love and closeness in marital relationships, feelings of protecting self and partners, and HIV counseling services. CONCLUSION: Efforts to improve HIV partner status notification in low- and lower-middle-income countries should consider barriers and facilitators across all its components, including notification, testing, and linkage to treatment. In addition, HIV partner services must be adapted to the unique needs of key populations.
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".