Hypersexuality in Kayakers: Roles of Sport, Gender, and Perceived Stress
Bibliographic record
Abstract
Sports participation is associated with heightened sexual behavior, while its determinants are unclear. We examined hypersexuality in 104 kayakers and 77 mixed exercisers, considering the roles of gender, exercise volume, and perceived stress. Participants, 89 men and 92 women (Mage = 26.1 ± 8.1 years) completed the Hypersexual Behavior Inventory, Perceived Stress Scale, and demographic questions online. Path analyses tested the relationships between perceived stress and exercise volume, considering gender and sports form-related differences. Women reported more stress than men (p < .001, Cohen’s d = .70). Men reported higher hypersexuality than women (p < .001, d = .96). Kayakers reported higher training volumes (p < .001, d = .97) and hypersexuality than mixed exercisers (p = .003, d = .46). Perceived stress was positively and moderately associated with hypersexuality, while exercise volume was positively but weakly related to hypersexuality only among men. Exercise volume was unrelated to hypersexuality in kayakers, while a positive, moderate association emerged in mixed exercisers. These results suggest that hypersexuality and its associations with perceived stress and exercise volume could vary based on the sports’ form and the gender of the athlete. While the perceived stress may relate to hypersexuality in both men and women, the relationship between exercise volume and hypersexuality may vary more according to gender and the sport’s form.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".