Exploring the self-perceived causes of eating disorders among Chinese social media users with self-reported eating disorders
Notice bibliographique
Résumé
Even though robust evidence suggests the high prevalence of eating disorders (EDs) in China, EDs in China are characterized by low diagnosis rates, delayed treatment-seeking, and ineffective treatments. Given that listening to patients’ perspectives and lived experiences is crucial to improving our understanding of EDs in the Chinese context, an investigation of the perceived causes of EDs in Chinese individuals with EDs represents a key step in improving the prevention and treatment of EDs in China. To explore the perceived causes of EDs based on data from a sample of Chinese social media users with self-reported EDs, with a particular focus on the Zhihu platform. We extracted and analyzed data through content analysis. Eight specific causes that could be classified into two groups were coded, including individual factors (e.g., “body image and eating”) and sociocultural factors (e.g., “media and cultural ideals”). A total of 2079 entries regarding self-reported EDs were retained for content analysis (14.7% were anorexia nervosa, 37.6% were bulimia nervosa, and 47.7% were binge-eating disorder). More than 90% of users with self-reported EDs claimed causes belonging to individual factors, while 35–51% of users claimed sociocultural factors. “Body image and eating” (68–87%) and “psychological and emotional problems” (65–67%) were the most commonly claimed specific causes, while “traumatic life events” (13–14%), “genetics and biology” (7–13%), and “sports and health” (9–12%) were the least claimed. Chi-square independent tests showed that users with different self-reported EDs disproportionately claimed certain causes. Using large-scale social media data, findings provide a deeper understanding of the perceived causes of EDs in the Chinese context from individuals with self-reported EDs and highlight the variations in perceived causes across different self-reported ED types. We explored the perceived causes of eating disorders (EDs) by using big data from Chinese social media (i.e., Zhihu) users with three self-reported ED types (i.e., anorexia nervosa, bulimia nervosa, and binge-eating disorder). Results showed that more than 90% of users with self-reported EDs claimed causes belonging to individual factors, while 35–51% of users claimed sociocultural factors. Users with different types of self-reported EDs disproportionately claimed specific perceived causes of their EDs. Our findings underscore the variations in perceived causes across different self-reported ED types. The study also highlights the utility and significance of researching the etiology of EDs via big datasets in the context of the evolving digital environment.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».