A socio-ecological analysis of factors influencing HIV treatment initiation and adherence among key populations in Papua New Guinea
Bibliographic record
Abstract
BACKGROUND: In Papua New Guinea (PNG) members of key populations, including female sex workers (FSW), men who have sex with men (MSM) and transgender women (TGW), have higher rates of HIV compared to the general adult population and low engagement in HIV care. This paper examines the socio-ecological factors that encourage or hinder HIV treatment initiation and adherence among HIV positive members of key populations in PNG. METHODS: As part of a larger biobehavioural survey of key populations in PNG, 111 semi-structured interviews were conducted with FSW, MSM and TGW, of whom 28 identified as living with HIV. Interviews from 28 HIV positive participants are used in this analysis of the influences that enabled or inhibited HIV treatment initiation and treatment adherence. RESULTS: Enablers included awareness of the biomedical benefits of treatment; experiences of the social, familial and health benefits of early treatment initiation and adherence; support provided by family and friends; and non-judgmental and supportive HIV service provision. Factors that inhibited treatment initiation and adherence included perception of good health and denial of HIV diagnosis; poor family support following positive diagnosis; and anonymity and stigma concerns in HIV care services. CONCLUSION: Exploring health promotion messages that highlight the positive health impacts of early treatment initiation and adherence; providing client-friendly services and community-based treatment initiation and supply; and rolling out HIV viral load testing across the country could improve health outcomes for these 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".