P-724 Hashtags, Hope, or Hype? A systematic review of fertility and reproductive health on social media platforms: A systematic literature review
Bibliographic record
Abstract
Abstract Study question To what extent are fertility and reproductive health topics discussed on social media and what is being discussed? Summary answer Evidence obtained from 157 studies, across 9 online platforms, and 13 countries, covered themes on fertility and pregnancy, psychosocial, sexual and reproductive health and ‘others.’ What is known already 1 in 6 individuals are affected by infertility globally. Despite this, the topic remains associated with a high level of stigma. In recent years, social media has become an influential channel for health information sharing, and an outlet for people dealing with fertility issues as well as a means of engaging with fertility and reproductive health topics. Individuals, patients, healthcare professionals (HCPs), educators, influencers and other stakeholder groups also use social media to disseminate information on reproductive health. This study therefore aimed to review fertility and reproductive health discussions on social media platforms to understand how information is disseminated. Study design, size, duration A systematic review was conducted according to Preferred Reporting Items for Systematic-Reviews and Meta-Analyses (PRISMA) guidelines. Databases Medline, PsycINFO, Emcare, and PubMed were searched for primary studies investigating fertility and reproductive health information on social media. Separate search strategies were conducted for fertility and reproductive health, and social media. Studies were analysed thematically and categorised. Participants/materials, setting, methods Inclusion and exclusion criteria were established using the Population, Intervention, Comparator and outcome (PICO) framework. Only studies published in English between January 2014, and December 2024 were included. Observational studies of any design were eligible for inclusion. Studies specifically focused on post-natal child health, parenting techniques, campaign implementation, and other media such as TV and newspapers were excluded. Main results and the role of chance A total of 157 studies were included. Countries included USA(52), UK(52), Canada(9), Switzerland(11), Netherlands(9), China(3), Brazil(3), Germany(2), Ireland(2), Jordan(1), Spain(1), South Korea(1), and Australia(1). Social media platforms reported included Facebook(37), YouTube(27), Twitter(26), Instagram(26), TikTok(6), Reddit(6), Forums(5), Blogs(4), Pinterest(2), Weibo(1), and others(17). Key themes and subthemes included: Fertility and pregnancy: (in)fertility, pregnancy loss, fertility treatments, pregnancy / complications, perinatal health male infertility, egg freezing, oncofertility, preterm birth, and childbirth, Sexual and reproductive health: women’s health, contraception, vaccination, sexual health, abortion, post-partum, nutrition, patient provider, underlying health conditions, non-invasive prenatal testing, sexually transmitted infections, HCPs, and maternal health. Psychosocial aspects: knowledge and awareness, mental health and support, body image, physical activity, communication. Others: Covid-19, fake news, medicine, drug and alcohol, finance, and stigma. Posts were authored by individuals, patients, HCPs and medical organisations. Stories of personal experiences or opinions received a higher rate of engagement compared with educational posts. Similarly, inspirational and support groups, and patient accounts had more engagement than HCPs, academic or fertility clinic accounts, despite the latter having better content quality. A number of studies found no significant difference in engagement between accurate and misleading information, raising the concern of misinformed decisions and health harming behaviours. Limitations, reasons for caution Of the studies included, some were self-reported, hindering the robustness of their conclusions. Only studies published in English were eligible for inclusion, thus limiting the generalisability of study findings. Wider implications of the findings Social media remains a powerful tool for understanding patient experiences of fertility and reproductive health. Experts must continue to engage with these platforms in order to amplify the positives and mitigate negative impacts such as misinformation, poor mental health and barriers to achieving reproductive intentions. Trial registration number 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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".