Dating Apps, Sexually Transmitted Infections and Risky Sexual Behaviour
Bibliographic record
Abstract
Over the last decade, the incidence rates of many sexually transmitted infections (STI) have been on the rise, especially amongst young adults. Popular Canadian media outlets have speculated that the reason behind these increases is the use of mobile dating applications which foster romantic and sexual connections. This cross-sectional study assesses whether students who use mobile dating apps are more or less likely to have been diagnosed with an STI in the previous 12 months and engage in risky sexual behaviour, compared to students who did not use mobile dating apps in the previous 12 months. An anonymous online questionnaire was used to collect data from 965 study participants currently enrolled at an Ontario university. The survey required participants to self-report STI testing behaviour and diagnoses, as well as sexual behaviours, including number of sexual partners, relationship type, condom use, substance use and sex work. I found that Ontario university students who used dating apps in the previous 12 months were more likely to have a greater number of sexual partners in the previous year (p<0.05), have multiple concurrent sexual partners (OR=10.72, 95% CI: 6.10-18.84), frequently use alcohol (OR=3.94, 95% CI:2.17-7.14) and cannabis (OR=3.36, 95% CI:1.45-7.78) in combination with sexual activity, and were more likely to have been tested for STIs in the previous 12 months (OR=2.25, 95% CI: 1.73-2.94) compared to non-dating app users. However, mobile dating app users were not more likely to have been diagnosed with an STI in the previous 12 months compared to non-dating app users.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".