Evaluation of the Sexual Health Behaviors of Black Male Adolescents and Young Adults Through Social Media Platforms: Web-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: Social media platforms such as Facebook, Instagram, and Twitter, which have millions of users who interact and communicate every day, have been effective in promoting sexual health interventions and in disseminating reproductive health education. They have also been shown to be useful in health promotion and have been used to track several key metrics (eg, comments, posts) among users of all demographics. However, there is a lack of research on the impact and reach of these social media platforms as a community-based tool for disseminating sexual health information and for increasing engagement among Black adolescents and young adults, which is a targeted high-risk population. OBJECTIVE: The purpose of this study was to determine the social media platforms and banner advertisements that affected engagement among Black male adolescents and young adults in participating in web-based health surveys. METHODS: A web-based survey was conducted from March 2019 to July 2019 to assess sexual health and health behaviors in a convenience sample of Black male adolescents and young adults in the age range of 18-24 years (N=170). Social media metrics from Facebook, Instagram, and Twitter were monitored. This cross-sectional survey comprised several categories, including basic personal information, drug-related risk behaviors, health care, sexual reproductive health questions, attitudes, norms, and perceived control, mental health, violence-related risk behaviors, and social media preferences. RESULTS: Social media advertisements on the Black Male Opinion survey reached approximately 146,412 individuals. Our primary finding of the web-based survey engagement was that referral (eg, group chat, indirect social media sharing) led to as the greatest proportion of recruitment, with Twitter and YouTube as the preferred sites to receive sexual health information. CONCLUSIONS: Recognizing the variety of technologies being used among Black male young adults and adolescents can help the community, researchers, and health care providers understand the web-based engagement of this high-risk population. This information may also promote culturally sensitive, customized marketing on sexual health information for this population.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".