Correlates of Tinder Use and Risky Sexual Behaviors in Young Adults
Bibliographic record
Abstract
Tinder is a frequently used geosocial networking application that allows users to meet sexual partners in their geographical vicinity. Research examining Tinder use and its association with behavioral outcomes is scarce. The objectives of this study were to explore the correlates of Tinder use and risky sexual behaviors in young adults. Participants aged 18-26 were invited to complete an anonymous online questionnaire between January and May 2016. Measures included sociodemographic characteristics, Tinder use, health related behaviors, risky sexual behaviors, and sexual attitudes. Associations among these variables were estimated using multivariate logistic regressions. The final sample consisted of 415 participants (n = 166 Tinder users; n = 249 nonusers). Greater likelihood of using Tinder was associated with a higher level of education (OR = 2.18) and greater reported need for sex (OR = 1.64), while decreased likelihood of using Tinder was associated with a higher level of academic achievement (OR = 0.63), lower sexual permissiveness (OR = 0.58), living with parents or relatives (OR = 0.38), and being in a serious relationship (OR = 0.24). Higher odds of reporting nonconsensual sex (OR = 3.22) and having five or more previous sexual partners (OR = 2.81) were found in Tinder users. Tinder use was not significantly associated with condom use. This study describes significant correlates of using Tinder and highlights a relationship between Tinder use with nonconsensual sex and number of previous sexual partners. These findings have salience for aiding public health interventions to effectively design interventions targeted at reducing risky sexual behaviors online.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".