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Record W4391477324 · doi:10.1080/26929953.2024.2310224

Hypersexuality in Kayakers: Roles of Sport, Gender, and Perceived Stress

2024· article· en· W4391477324 on OpenAlexaff
Attila Szabó, Beáta Bőthe, Margeréta Lazur, Florence Tremblay, Rita Kovácsik

Bibliographic record

VenueSexual Health & Compulsivity · 2024
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyStress (linguistics)HypersexualityDevelopmental psychologySexual behavior

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.395
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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