Understanding Experiences of and Unmet Needs in Online Searches for Menopause Information: An Exploratory Survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».