(215) Use of Social Media for Participant Recruitment in Sexual Medicine Research
Bibliographic record
Abstract
Abstract Introduction Recruiting participants for sexual medicine research presents unique challenges due to stigma, privacy concerns, and the need for targeted outreach to niche populations. Social media platforms can serve as a tool to reach geographically dispersed and diverse communities. Objective Examine the use of social media in participant recruitment and assess trends, strategies, and platform efficacy in published literature. Methods A keyword search was conducted on all peer-reviewed articles published in the Journal of Sexual Medicine from 2015-2025 using terms including “social media,” “social media recruitment”, “Facebook,” “Instagram,” “Twitter,” “Reddit,” “Snapchat,” “WhatsApp,” “TikTok,” and “Snapchat.” Of the 208 articles screened, studies were included if social media platforms were utilized to recruit participants. For eligible studies, recorded data included year of publication, number of participants, research topic, platform utilization, and countries from which participants were recruited. Results A total of 42 studies published between 2015 and 2025 used social medial platforms to recruit participants. Social media enables large-scale recruitment with studies reporting an average of 2,285 participants from 25 different countries represented. Of these, 43% included participants from the USA and/or Canada only. Most studies (67%) were published after 2021, indicating a recent surge in the use of social media for recruitment. Facebook was the most utilized platform, appearing in 83.3% of studies, followed by Twitter (21.4%), Instagram (19.0%), Reddit (16.7%), WhatsApp (11.9%), and WeChat (7.1%). (FIGURE 1) Remaining platforms (4.8%) included Amazon Mechanical Turk, Zhihu, and Weibo. Nearly half of the studies (47.6%) leveraged two or more social media platforms concurrently. Recruitment strategies varied by topic: studies on menopause, female orgasm, and body image leveraged Facebook groups, while those exploring sexual behavior or psychogenic issues more frequently used Reddit or Instagram to reach younger or anonymous audiences. Conclusions Social media has potential to enhance participant recruitment in sexual medicine research, particularly among diverse and often difficult to reach, or stigmatized populations. While Facebook remains widely used, the integration of globally accessible and culturally relevant platforms like WhatsApp and WeChat illustrates the opportunity to reach international audiences. The increasing usage of Instagram highlights the evolving trends in the use of social media platforms for recruitment. Future research studies should consider the benefits of social media to advance equity and inclusivity in sexual medicine research. Disclosure No
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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 | 0.044 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".