Reproductive Health Experiences Shared on TikTok by Young People: Content Analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: TikTok is a popular social media platform that allows users to create and share content through short videos. It has become a place for everyday users, especially Generation Z users, to share experiences about their reproductive health. Owing to its growing popularity and easy accessibility, TikTok can help raise awareness for reproductive health issues as well as help destigmatize these conversations. OBJECTIVE: We aimed to identify and understand the visual, audio, and written components of content that TikTok users create about their reproductive health experiences. METHODS: A sampling framework was implemented to narrow down the analytic data set. The top 6 videos from each targeted hashtag (eg, #BirthControl, #MyBodyMyChoice, and #LoveYourself) were extracted biweekly for 16 weeks (July-November 2020). During data collection, we noted video characteristics such as captioning, music, likes, and cited sources. Qualitative content analysis was performed on the extracted videos. RESULTS: The top videos in each hashtag were consistent over time; for example, only 11 videos appeared in the top 6 category for #BirthControl throughout the data collection. Most videos fell into 2 primary categories: personal experiences and informational content. Among the personal experiences, people shared stories (eg, intrauterine device removal experiences), crafts (eg, painting their pill case), or humor (eg, celebrations of the arrival of their period). Dancing and demonstrations were commonly used in informational content. CONCLUSIONS: TikTok is used to share messages on myriad reproductive health topics. Understanding users' exposure provides important insights into their beliefs and knowledge of sexual and reproductive health. The study findings can be used to generate valuable information for teenagers and young adults, their health care providers, and their communities. Producing health messages that are both meaningful and accessible will contribute to the cocreation of critical health information for professional and personal use.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it