What constitutes a syndemic? Methods, contexts, and framing from 2019
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to describe what methods were used for 60 articles on HIV syndemics in 2019, where they took place, what syndemic clusters emerged, and why this matters. RECENT FINDINGS: Most articles published in 2019 used regression analyses, and fewer used higher level modeling techniques, frequencies and descriptive, longitudinal cohort study, and social network analysis. Some employed ethnography, qualitative interviews, or were simply reviews. Most syndemic co-factors were substance abuse, risky sexual behavior, depression, intimate partner violence, stigma, sexually transmitted infections, and trauma and non-communicable diseases. Half of the studies were conducted in the United States and mostly in urban areas. Other contexts were Canada, Kenya, Uganda, Liberia, Nigeria, South Africa, and Botswana, Jamaica, Dominican Republic, India, Indonesia, China, Peru, and Romania. SUMMARY: Most recommendations suggested that people living with HIV need interventions that address other factors situated within their life, such as their mental health, social stigma, experiences of trauma and intimate partner violence, and social stigma and sexual risk taking. Many took an intersectoral approach and emphasized the need to consider the various factors that shape experiences with HIV, from sex, sexuality, class, race and ethnicity, and past trauma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".