Dimensions of sexual experiences reflected through adjective selection: findings from a US nationally representative survey
Bibliographic record
Abstract
BACKGROUND: A growing body of research focuses on the quality of sexual experiences, such as the importance of sexual pleasure, comfort, and intimacy for overall health. Building upon this work, this study aims to develop a deeper understanding of the dimensions of sexual experiences using data from 2897 adult participants from the 2018 National Survey of Sexual Health and Behavior (NSSHB; 1314 men, 1583 women). METHODS: We conducted an exploratory factor analysis (EFA) to examine the extent to which 20 adjectives describing adults' most recent sexual experience (e.g. boring, playful, romantic, etc.) formed a coherent factor or several factors. Next, we explored how different scores on each factor were associated with sexual outcomes for women and men. RESULTS: Our EFA generated two explanatory factors that mapped onto two underlying components: sexual pleasure and sexual danger. These two factors were correlated with sexual health outcomes including sexual wantedness, orgasm, self-rated sexual health, meaningfulness of sex and pain during sex. CONCLUSIONS: We found that the innovative list of adjectives used in the 2018 NSSHB provided important and reliable insight into latent dimensions of sex. Specifically, we found that the pleasure dimension was important for both genders, and especially for women, in experiences where the sex was wanted, orgasmic, meaningful, healthy, and without pain. Implications for sexual experiences and suggestions for future research are discussed.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".