A Cross-Country Comparison of Reasons for Condom Use during Menses: Associations with Age and Gender Inequality
Bibliographic record
Abstract
Objectives: Despite evidence that menstrual bleeding is a risk factor for sexually transmissible infections, few studies have assessed the prevalence of, and reasons for, condom use during menses. The objectives of the present study were to examine (1) the prevalence of condom use during menses; (2) if condom use during menses varies depending on age and gender inequality of country of residence; and (3) whether age and gender inequality of country of residence interact with reasons for using, and not using, condoms during menses. Methods: A sample of 25,955 individuals from 146 countries, all reporting penile-vaginal intercourse in the past 3 months, was used. Condom use during menses over the previous 3 months, whether this varied by age and level of gender inequality in countries, and reasons for using and not using condoms during menses were assessed via a web-based questionnaire. Results: Age and gender inequality of country of residence were significant predictors of condom use during menses, with those in the younger, high gender-equality group significantly the most likely, and those in the older, low gender-equality group, the least likely to use condoms during bleeding. The three most reported reasons were “for contraception,” “I use condoms even when I don’t have my period,” and “protecting your partner from your blood.” Reported reasons for using and not using condoms during menses showed significant associations with age and with level of gender inequality. Conclusion: Findings highlight that globally sexually transmitted infection/HIV education programs need to promote consistent condom use across the menstrual cycle.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".