International Sex Survey: Study protocol of a large, cross-cultural collaborative study in 45 countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Limitations of research into sexuality and compulsive sexual behavior disorder (CSBD) include the use of simplistic methodological designs and the absence of quality and unified measurements, empirically supported theoretical models, and large, collaborative studies between laboratories. We aim to fill these gaps with the International Sex Survey (ISS, http://internationalsexsurvey.org/). METHODS: The ISS is a large-scale, international, multi-lab, multi-language study using cross-sectional survey methods, involving more than 40 countries. Participants responding to advertisements complete a self-report, anonymous survey on a secure online platform. Collaborators from each country collect a community sample of adults with a minimum sample size of 2,000 participants with a gender ratio of approximately 50-50% men and women, including diverse individuals with respect to sexuality and gender. The ISS includes a wide range of sociodemographic questions and scales assessing a diverse set of sexual behaviors, pornography use, psychological characteristics, and potential comorbid disorders. Analyses are conducted within a structural equation modeling framework, including variable (e.g., measurement invariance tests) and person-centered approaches (e.g., latent profile analysis). DISCUSSION AND CONCLUSIONS: The ISS will provide well-validated, publicly available screening tools, helping to eliminate significant measurement issues in the field of sexuality research and health care. It will provide important insights to improve the theoretical understanding of CSBD as well as help to identify empirically supported treatment targets for prevention and intervention programs. Following open-science practices and making study materials open-access, the ISS may serve as a blueprint for future large-scale research in addiction and sexuality research.
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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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".