Sexual mixing patterns in men who have sex with men: network approaches for smart resource allocation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Age-based sexual mixing patterns in men who have sex with men (MSM) can greatly inform strategic allocation of intervention resources to subsets of the population for the purpose of preventing the greatest number of new HIV infections. METHODS: Egocentric network data collected from MSM participating in annual HIV sentinel surveillance surveys were used to assess age-dependent mixing and to explore its epidemiological implications on the risk of HIV transmission risk (among those HIV-infected) and HIV acquisition risk (among those not infected). RESULTS: Mixing in this sample of 1605 Chinese MSM is relatively age assortative (the average of values expressing the degree of preferential mixing were 2.01 in diagonal cells vs 0.87 in off-diagonal cells). Expected numbers of HIV acquisition were highest in the 20-24years age group; those for HIV transmissions were highest among 25-29year olds. The risk of both acquisition and transmission was highest in age groups that immediately follow the most commonly reported ages of sexual debut in this population (i.e. age 20). CONCLUSIONS: These findings suggest that combination prevention resources should be targeted at younger MSM who are at higher risk of both transmission and acquisition. Programs may also do well to target even younger age groups who have not yet debuted in order to establish prevention effects before risky sexual behaviours begin. More research on optimal strategies to access these harder-to-reach subsets of the MSM population is needed. Findings also support ongoing efforts for public health practitioners to collect network data in key populations to support more empirically driven strategies to target prevention resources.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it