A web of risk: multilevel factors and feedback loops (re)produce HIV ‘risk’ among gay, bisexual and other men who have sex with men – a global systematic review
Bibliographic record
Abstract
BACKGROUND: HIV literature shows that gay, bisexual and men who have sex with other men (GBMSM) experience inequities across social and contextual factors. Given growing inequities, this study used complex systems theory, a scientific approach to understanding the interconnected parts, to identify and visualise the system of factors that shape the emergence or (re)production of HIV risk among GBMSM. METHODS: A meta-synthesis of systematic reviews and meta-analyses was conducted to examine risk factors for HIV in alignment with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria and quality assessments using A Measurement Tool to Assess Systematic Reviews 2. After screening 255 studies, data were synthesised and visualised from 29 articles with moderate-quality or high-quality assessments. Study characteristics and risk factors for HIV were extracted, and data were thematically analysed into higher-order themes and respective subthemes aligned with Bronfenbrenner's socio-ecological model. Kumu.io, a system mapping software, was used to visualise the system of factors. RESULTS: Our thematic analysis and visualisation portray a dynamic and complex web of HIV risk that GBMSM experience implicated across all levels of the socio-ecological model: individual, interpersonal, community, institutional/organisational and structural/policy levels. These risk factors, in tandem, interact with one another to create pathways and patterns that generate feedback loops, such that the systems of factors create the emergence of GBMSM's HIV risk beyond that accounted for at the individual level. CONCLUSION: GBMSM's HIV risk is socially patterned by a diversity of multilevel and interacting risk factors, which creates a dynamic and reinforcing system of HIV risk that requires attention in its totality to fully address HIV risk.
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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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".