Exploring sexual motivations underlying substance use: Gender perspectives, substance categories and substance use disorder severity
Bibliographic record
Abstract
Sex under the influence of substances is a common sexual practice. Although they could explain engagement in sex under the influence of substances, few studies have explored sexual motivations driving substance use. To bridge this knowledge gap, this study aims to investigate differences in sexual motivations across genders and substance categories, and the link between substance use severity and sexual motivations. 188 adults (aged 18–74) took part in a comprehensive assessment interview addressing substance use Quebec Integrated Addiction Assessment – QIAA, including three sexual motivation items for three substance categories (alcohol, cannabis, other substances), and substance use severity World Mental Health Composite International Diagnostic Interview (WMH-CIDI). Wilcoxon rank-sum tests and generalized estimating equations were used to compare sexual motivations among substance categories between men and women. A logistic regression assessed whether substance use severity could influence the presence of sexual motivations when considering gender and age. Results revealed an interaction between gender and substance categories on sexual motivations. Men were more likely to present sexual motivations and exhibited greater sex-related substance use motivations than women for substances other than alcohol and cannabis. Moreover, higher substance use severity scores were associated with the presence of sexual motivations for substance use. This study underscores the need to develop gender-sensitive interventions that consider sexual motivations underlying substance use. Consideration of sexual motivations could help identify the needs fulfilled by substance use and prevent triggers arising from sexual situations. Being among the first studies on the subject, continuing research in this area to further investigate sexual motivations underlying substance use remains essential.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".