TikTok as a Resource for Fertility Information and Support: A Patient Survey
Bibliographic record
Abstract
Objective: TikTok is a rapidly growing social media platform with over 800 million users worldwide. Many patients access fertility-related content across social media platforms, however, this has never been studied related to TikTok. This study aimed to describe patient perspectives and experiences using TikTok for fertility-related content. Materials and methods: We conducted a cross-sectional web-based survey from April 1st 2023 to October 1st 2023 at a large fertility center in Toronto, Canada. Patients were eligible for inclusion if they self-identified that they use TikTok for fertility-related content and had pursued any form of fertility care. Results of the survey were described with descriptive statistics and thematic analysis. Results: A total of 23 patients with a mean age of 36.74±6.67 years participated in the online survey. Fertility-related TikTok content included lived experiences of fertility journeys descriptions of fertility treatments or procedures, live-streaming of fertility treatments or procedures, interactive questions and answers, and educational videos. Creators of fertility-related TikTok content include patients undergoing fertility treatments, physicians, naturopaths, counselors, and patient advocates. The most common reasons for liking TikTok for fertility content included empathy or shared experiences, stress relief, and self-education. Reasons for disliking TikTok for fertility information included misinformation, commercialization or advertisements, and negative emotions of stress, anxiety, or emotional upset. Misconceptions seen on TikTok included misinformation about complications and success rates for assisted reproductive therapy, as well as nutritional advice. Conclusion: Fertility providers should have a growing awareness of information available on TikTok for patients accessing fertility care and assisted reproductive technology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".