316 - Urinary incontinence discussions on Instagram: A hashtag analysis of top posts and reels
Notice bibliographique
Résumé
Hypothesis / aims of study Social media use has skyrocketed. With more than half of the world’s population participating in social media, these platforms have become spaces for individuals to seek information, share experiences, and engage in discussions about health-related topics. With over 1.4 billion users, Instagram is one of the most widely used platforms with user-generated content. This platform’s interactive nature may encourage users to share personal experiences, engage with educational resources and explore treatment options while providing a sense of anonymity, enabling individuals to openly discuss stigmatized health conditions. Given Instagram’s role in health communication, this study aimed to explore how UI is represented on Instagram and to understand the role it might play in awareness, education, and discourse surrounding UI. Study design, materials and methods A list of eighteen hashtags was developed with expert consultation and Instagram’s related-search functionalities. The 28 Instagram-generated top posts and reels under each hashtag were analyzed. Posts or reels before 2019 were not included to ensure recency of data, and content not in English was excluded. Data were gathered from July to August, 2024, to minimize algorithmic updates or changes in engagement trends. Quantitative data were gathered for each post, including likes, comments, views (for reels), and the number of followers of the post creator. Details such as media type (static post or video), captions, content description, posting date, creator's username, and authorship background were recorded for analysis. Engagement rates were examined and compared across categories to identify the most popular and engaging type of content by calculating the mean likes in each content category. Posts and reels were categorized into content categories including advertisements (promotional content for products or services), educational content (informative posts including management and treatment tips), personal stories (user-shared experiences about living with or managing UI), humor (jokes or memes about UI), research/academia (including posts about panel discussions and published articles), and unrelated to UI (posts under relevant hashtags, but not addressing UI). Furthermore, authorship categories included healthcare professionals, wellness instructors, businesses and other. Results Categories of content included education (46%, n=207), advertisements (41%, n=182), humour (6%, n=27), personal stories (3%, n=13), research/academia (3%, n=14), and unrelated to UI (1%, n=6). Healthcare professionals contributed 56% of the educational content (116 of 207 posts). The authorship categories included businesses (19%, n=82), healthcare professionals (40%, n=177), wellness instructors (10%, n=45), and other (31%, n=139). The median likes for each category were advertisements (n=22), educational (n=49), humor (n=59), personal stories (n=74), academia/research (n=16), and unrelated to UI (n=335). Interpretation of results Results indicated that Instagram is a likely significant platform for UI-related education. Education and advertisements were the most common categories of content, revealing Instagram’s role in informing and promoting. Engagement data suggested discomfort with UI, an interest in “personal stories”, and the effect of humor in capturing attention. The high engagement with personal stories suggests that users value firsthand experiences, reinforcing the importance of patient narratives in health discussions. Concluding message Instagram is a pertinent tool for disseminating information on UI. Healthcare professionals’ engagement with this platform may add to the credibility of posts, while focusing on engaging content to improve outreach. Given trends, efforts to increase patient narratives and destigmatize UI through social media campaigns could prove highly effective in enhancing public awareness and education. Download: Download high-res image (64KB) Download: Download full-size image Figure 1 . Download: Download high-res image (223KB) Download: Download full-size image Figure 2 . Funding None Clinical Trial No Subjects None
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 | 0,001 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».