Safe Sex Messages Within Dating and Entertainment Smartphone Apps: A Review
Bibliographic record
Abstract
BACKGROUND: Smartphone apps provide a new platform for entertainment, information distribution, and health promotion activities, as well as for dating and casual sexual encounters. Previous research has shown high acceptability of sexual health interventions via smartphone apps; however, sexual health promotion apps were infrequently downloaded and underused. Integrating sexual health promotion into established apps might be a more effective method. OBJECTIVE: The objective of our study was to critically review popular sex-related apps and dating apps, in order to ascertain whether they contain any sexual health content. METHODS: Part 1: In January 2015, we used the term "sexual" to search for free apps in the Apple iTunes store and Android Google Play store, and categorized the sexual health content of the 137 apps identified. Part 2: We used the term "dating" to search for free geosocial-networking apps in the Apple iTunes and Android Google Play stores. The apps were downloaded to test functionality and to determine whether they included sexual health content. RESULTS: Part 1: Of the 137 apps identified, 15 (11.0%) had sexual health content and 15 (11.0%) contained messages about sexual assault or violence. The majority of the apps did not contain any sexual health content. Part 2: We reviewed 60 dating apps: 44 (73%) targeting heterosexual users, 9 (15%) targeting men who have sex with men (MSM), 3 (5%) targeting lesbian women, and 4 (7%) for group dating. Only 9 dating apps contained sexual health content, of which 7 targeted MSM. CONCLUSIONS: The majority of sex-related apps and dating apps contained no sexual health content that could educate users about and remind them of their sexual risks. Sexual health practitioners and public health departments will need to work with app developers to promote sexual health within existing popular apps. For those apps that already contain sexual health messages, further study to investigate the effectiveness of the content is needed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".