A population's higher-risk sexual behaviour is associated with its average sexual behaviour—An ecological analysis of subpopulations in Ethiopia, Kenya, South Africa, Uganda and the United States
Bibliographic record
Abstract
BACKGROUND: Given the large variation in the prevalence of sexually transmitted infections between populations it is important to characterize how sexual vary between populations. In this paper we assess how the distribution of lifetime sexual partners varies between populations. We ask: do populations with higher mean lifetime sexual partners merely differ by virtue of the presence of a core-group with increased number of partners or do the population distributions move as more coherent wholes? METHODS: We defined those in the top decile of lifetime sex partners in each country as constituting the higher-risk behaviour group (HRB). Spearman's correlation was used to evaluate the relationship between mean lifetime partners and prevalence of those in the HRB by ethnic group within Ethiopia, Kenya, South Africa, Uganda and the United States. In each case, data from nationally representative surveys were used. Two-sample Wilcoxon rank-sum were applied to test if there was a difference in the number of lifetime partners between the highest and lowest-risk subpopulations, separately for men and women. To account for autocorrelation, all analyses were conducted using means/medians excluding those in the HRB. A P-value of <0.05 was considered statistically significant. RESULTS: In each country, a positive correlation existed between subpopulations' mean lifetime partners and the prevalence of the HRB. Spearman's correlation varied from 0.20 to 0.99 for men and 0.74 to 1.0 for women. This association was statistically significant in 6 out of 10 comparisons. There was a statistically significant difference in the number of lifetime partners between the highest and lowest-risk ethnic groups in all countries except South Africa, where the difference was only significant for women. CONCLUSIONS: Our results suggest that sexual behaviours vary coherently between different populations. As such, sexually transmitted infection control efforts would benefit from including both targeted campaigns focusing on the HRB and population-strategies that seek to address factors responsible for high mean risk behaviour.
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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 | 0.002 |
| 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".