Assortativity and Mixing by Sexual Behaviors and Sociodemographic Characteristics in Young Adult Heterosexual Dating Partnerships
Bibliographic record
Abstract
BACKGROUND: Assortative sexual mixing, the tendency for individuals to choose partners with similar characteristics to themselves, may be an important contributor to the unequal distribution of sexually transmitted infections in populations. We analyzed the tendency for assortative mixing on demographic and sexual behaviors characteristics in newly formed young adults dating partnerships. METHODS: Women aged 18 to 24 years and their male sexual partners of no more than 6 months were recruited during 2005 to 2010 at universities in Montreal, Canada. New dating partners were also prospectively recruited during the 2-year follow-up. We used Spearman and Newman coefficients to examine correlations between partners' demographic characteristics and sexual behaviors, and multivariable logistic modeling to determine which characteristics were assortative. RESULTS: Participants in 502 recruited partnerships were assortative on age (Spearman P = 0.60), smoking behavior (P = 0.43), ethnicity (Newman coefficient=0.39), lifetime number of sexual partners (P = 0.36), sex partner acquisition rates (P = 0.22), gap length between partnerships (P = 0.20), and on whether they had concurrent partners (P = 0.33). Partners were assortative on number of lifetime partners, sexual partner acquisition rates, concurrency, and gap length between partnerships even after adjustment for demographic characteristics. Reported condom use was correlated between initial and subsequently recruited partners (P = 0.35). There was little correlation between the frequencies of vaginal/oral/digital/anal sex between subsequent partnerships. CONCLUSIONS: Dating partnerships were substantially assortative on various sexual behaviors as well as demographic characteristics. Though not a representative population sample, our recruitment of relatively new partnerships reduces survivor bias inherent to cross-sectional surveys where stable long-term partnerships are more likely to be sampled.
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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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".