Examining the Components of Population-Level Sexual Behavior Trends From 1993 to 2007 in an Open Ugandan Cohort
Bibliographic record
Abstract
INTRODUCTION: Sexual behavior changes are widely cited as contributing factors to sexually transmitted disease trends. We explore a rarely examined aspect of behavior trends in an open cohort--the relative impact of individuals' changing reported behavior versus new responses due to a changing respondent base. METHODS: Respondents from an open cohort in Uganda annually answer questions on sexual behavior. We describe the impacts on behavior trends of: respondents' changing reported behavior, migration, mortality, changing eligibility for indicator inclusion, changing survey participation, and misreporting. We report contributions to trends on the following factors: condom use, ever had sex, age at first sex, and number of sexual partners and casual partners. RESULTS: Main trend contributions varied by indicator. Condom use trends were influenced by individuals' changing responses and by increasing condom use among in-migrants and newly interviewed people. Sexual partners were driven by fewer partners among newly interviewed people, although increase of partners in 1999, 2004, and 2006 stemmed mainly from people changing answers. Thirty-nine percent of responses to age at first sex among 17- to 20-year-olds were inconsistent--different ages in different years. Early trends in the factor "ever had sex" among 15- to 19-year-olds were driven by people changing their answers--including ever to never, an impossible sequence. Comparing behavior in one year to mortality in the next, we found little evidence of higher mortality among higher risk takers. DISCUSSION: In an open cohort, various factors contribute to sexual behavior trends. When reporting sexual behavior trends, researchers should acknowledge the contributing factors and attempt to separate the role of interindividual versus intraindividual changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".