Effects of Social Media Narratives on Affective and Behavioural Responses to Menopause Content: A Randomised Online Experiment with UK Perimenopausal and Post-Menopausal Women (Preprint)
Bibliographic record
Abstract
BACKGROUND: Social media is an increasingly prominent channel for communicating menopause information and experiences, yet the affective and behavioral consequences of different narrative framings remain unclear. OBJECTIVE: We examined how distress, normalizing, and transformative narratives influenced women's immediate responses to menopause content online, drawing on established narrative framings of menopause as normality, distress, and transformation. METHODS: In an online experiment, UK women aged 40 to 83 years who were perimenopausal or postmenopausal were recruited via Prolific, a widely used online recruitment platform for behavioral and social science research. A total of 737 women were randomly assigned to view 4 anonymized and standardized social media posts from a pool of 12 reflecting 1 of 3 narratives: normal (n=248, 33.6%), distress (n=241, 32.7%), or transformative (n=248, 33.6%). Participants then reported affective reactions, expected behavioral responses, and perceptions of the posts using 5-point ordered response scales. Ordinal logistic regression models tested demographic predictors and condition effects controlling for demographic factors. RESULTS: Participants who viewed distress-framed posts reported greater levels of worry (β=.910; P<.001), confusion (β=.818; P<.001), and anxiety (β=.817; P<.001) and lower levels of reassurance (β=-.970; P<.001), optimism (β=-.708; P<.001), and empowerment (β=-.540; P<.001). Distress framing also increased perceived knowledge of menopause (β=.564; P<.001) despite participants feeling more negatively toward the posts. Neither distress nor transformative narratives influenced expected behavioral intentions to like, share, save, comment on, search for, or discuss social media posts compared with normalizing narratives. Postmenopausal status (β=-.630; P<.001) and older age (β=-.492; P<.001) were independently associated with less worry and anxiety. Participants rated distress (β=-.806; P<.001) and transformative posts (β=-.968; P<.001) as less representative of health professionals than normalizing posts; transformative posts were also judged to be less representative of newspapers or television (β=-.687; P<.001). CONCLUSIONS: Narrative framing shaped immediate affect but not intended engagement with menopause content. Because this study assessed short-term responses to controlled, standardized posts, future research should examine whether these effects persist over time and how they operate in more ecologically valid social media environments. As public discussion expands, diverse, balanced narratives may help reduce stigma and temper the disproportionate salience of negative framing. This study advances understanding of how narrative framing shapes responses to health content online.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".