Concordance of self-reported sexual intercourse frequency between members of mixed-sex couples attempting conception
Bibliographic record
Abstract
Studies about sexual health require accurate information on sexual behaviors, yet there is no gold standard for assessing sexual behavior. Concordance of partner reports is one way to estimate the reliability and, indirectly, the validity of such data. We aimed to evaluate the inter-partner concordance of self-reported intercourse frequency among mixed-sex couples attempting conception. We analyzed data from Pregnancy Study Online (PRESTO), a North American prospective preconception cohort study. During 2013-2021, self-reported intercourse frequency at baseline was ascertained using the same question for both partners: "In the past month, about how often did you have sexual intercourse with your partner?" with categorical response options. We used unweighted and linear-weight weighted kappas to assess inter-partner concordance of reported intercourse frequency and log-binomial regression to estimate unadjusted and adjusted prevalence ratios (PR) and 95% confidence intervals (CI) for predictors of discordance. Among 3,015 couples, 1,927 (63.9%) reported exactly concordant categories of intercourse frequency, while the female partner reported more frequent intercourse in 715 (23.7%) couples and the male partner reported more frequent intercourse in 373 (12.4%) couples. Unweighted and weighted kappas were 0.50 (95% CI 0.48, 0.53) and 0.63 (95% CI 0.61, 0.65), respectively. Predictors of discordance included marital status (unmarried versus married: PR=1.61 [95% CI 1.11, 2.29] for the male partner reporting more frequent intercourse) and longer relationship length (5-9 years PR=1.14 [95% CI 0.96, 1.34], ≥10 years PR=1.14 [95% CI 0.92, 1.42], respectively, compared with <5 years) for the female partner reporting more frequent intercourse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".