Understanding socio-sexual networks: critical consideration for HIVST intervention planning among men who have sex with men in Kenya
Bibliographic record
Abstract
BACKGROUND: HIV self-testing (HIVST) has emerged as a way of reaching individuals who may be less likely to access testing, including men who have sex with men (MSM). Understanding the social networks of MSM is key to tailoring interventions, such as HIVST, for particular locations. METHODS: We undertook a socio-sexual network study to characterize and identify patterns of connection among MSM and inform an HIVST intervention in three sites in Kenya. Community researchers in each site selected eight seeds to complete a demographic form and network surveys for 15 each of their sexual and social network members. Seeds recruited three respondents, including two regular service users and one MSM who was "unreached" by the program, who then each identified three respondents, resulting with data on 290 individuals. RESULTS: Findings illustrate the interconnectedness of community-based organization (CBO) members and non-members. In networks where a majority of members had a CBO membership, members had better contacts with programs and were more likely to have accessed health services. Larger networks had more HIV testing and seeds with frequent testing had a positive influence on their network members also being tested frequently. HIVST was tried in very few networks. Almost all network members were willing to use HIVST. CONCLUSION: Willingness to use HIVST was nearly universal and points to the importance of networks for reaching individuals not enrolled in programs. Network analysis can help in understanding which type of networks had higher testing and how network-based approaches can be useful to promote HIVST in certain contexts.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".