Understanding Experiences of and Unmet Needs in Online Searches for Menopause Information: An Exploratory Survey
Bibliographic record
Abstract
BACKGROUND: Menopause is a significant time in a woman's life, but only recently has there been an open discussion about it in the media, workplaces, and general society. With increasing frequency, women are using the internet to research menopause, making it essential that online sources provide safe, high-quality, and relevant information. OBJECTIVE: This study aimed to investigate the current state of the online information landscape for menopause from the perspective of information seekers, exploring (1) information-seeking behavior and (2) perceptions of online resources for menopause. METHODS: A 10- to 15-minute online survey was conducted asking about the respondents' use of and opinions about online resources specifically for menopause. We distributed the survey via social media, email, and word of mouth. Quantitative data were explored using means and frequencies. Group differences between menopausal groups were analyzed using chi-square, Fisher exact, or Kruskall-Wallis tests as appropriate. Qualitative data were analyzed using data-driven thematic analysis. RESULTS: Data from 627 participants were analyzed (early perimenopause: n=171, 27.3%, late perimenopause: n=125, 19.9%, postmenopause: n=262, 41.8%, and surgical menopause: n=69, 11%). The majority of respondents had used the internet as a source of information (581/627, 92.7%), with the internet being the first choice of information source (489/581, 84.2%). The most searched-for information online was about menopause symptoms (479/581, 82.4%), menopause treatment options (442/581, 76.1%), and self-help tips or strategies (318/581, 54.7%). The majority of participants trusted online information to some extent (615/627, 98.1%), with many also considering online information accurate to some extent (555/627, 88.5%). Many participants reported finding some but not all of the information they were looking for online (379/581, 65.2%). Thematic analysis revealed 10 themes related to information quality and accessibility and sought-after information (eg, symptom specifics, treatment, and nonformal management strategies). Analysis also indicated that information is lacking for several groups, including those in medically induced or surgical menopause. CONCLUSIONS: The study showed that online informational resources are widely accessed and widely perceived as useful and trustworthy. However, it is crucial that the quality of online information is evaluated, especially considering the large number of users who rely on it as their first or only informational source. Online searches were usually performed to find information related to symptoms, treatment, and self-help recommendations, with differences in search behaviors observed across menopausal stages and groups, highlighting the need for tailored informational resources. Thematic analysis revealed gaps in the provision of online information both in terms of content and quality. Participants noted a lack of comprehensive symptom information, inadequate information for groups such as those experiencing medical or surgical menopause, and concerns about outdated content and a lack of source transparency. Future research with more diverse samples is needed to better understand variations in online health information-seeking behaviors across groups.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".