Side effects and cessation of the oral contraceptive pill on TikTok: a content analysis
Bibliographic record
Abstract
Objectives: This study aimed to assess the content and reliability of videos discussing the oral contraceptive pill (OCP) on TikTok, the popular social media platform amongst adults aged 18-24, to gauge the dialogue surrounding birth control on TikTok. Methods: We conducted a quantitative content analysis. The top 100 TikTok videos in English under each of the six hashtags related to OCPs were collected. Video content, engagement metrics (likes, comments, shares), and creator attributes were analyzed by two independent reviewers, with a third to arbitrate discrepancies. Results: 307 videos were included in the final data set with an average of 134,891 likes, 1,080 comments, and 7,483 shares. Healthcare providers created 27% of videos and 85.5% of these videos were educational. The majority of videos (73%) were created by non-healthcare providers and 54.4% discussed OCPs in a negative tone. Side effects were mentioned in 79% of videos, and 64% of these videos carried a negative tone regarding OCP side effects. Discontinuing OCPs was discussed in 24% of videos, and 83% of these videos carried a negative tone. Conclusions: The most frequently discussed topic was the side effects of OCPs, with the majority framed negatively. Approximately one quarter of videos addressed discontinuing OCPs, often portraying cessation as beneficial. In the post-Roe v. Wade era, understanding how OCP experiences are portrayed on TikTok highlights the importance of physician-patient collaboration to support informed contraceptive decision-making and move beyond narratives that focus primarily on negative experiences.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".