Eating Disorder Social Network Density: Its Impact on Diagnosis and Recovery
Notice bibliographique
Résumé
Aims: A handful of studies argue that ED treatment would benefit from a network analysis of social influences, particularly in girls and young women pressured by socio-culturally prescribed beauty standards, and reinforced by peers and family. The study’s aim was to investigate the impact of social network density on a person’s acquisition, perpetuation, and recovery from an eating disorder (ED). It was hypothesized that one’s connectedness within dense social networks of others with EDs would increase the likelihood of an ED diagnosis and resistance to treatment and recovery. Methods: One thousand participants, largely from North America and Europe, completed an online survey of ED social networks. Respondents were asked whether they had an ED diagnosis, and if so, the diagnosis/typology, whether they knew others with an ED, whether they were in recovery, and, if so, the extent of social supports. Indices and latent structural equation model (SEM) variables were constructed from respondents’ identification of siblings, peers, friends, parents, other relatives, spouses, and neighbours with an ED. Similar indices were constructed for others identified as supportive of recovery. Social media influence was measured by asking if pro-anorexic or recovery websites were viewed. Data were analysed using bivariate statistics and Lavaan’s SEM R program. Results: Social network density (knowing others with EDs) was highly predictive of ED diagnosis, including multiple EDs. Internet media was equally impactful. Same-sex siblings and peers had the greatest influence, exceeding parents or other relatives/friends. Networks of supportive others were highly predictive of recovery, outweighing negative ED models and media. Conclusion: Our results were highly revealing of dense networks of family and peer models of EDs as well as supportive networks for recovery. The density/richness of social networks of others with an ED was highly predictive of an ED diagnosis, particularly of multiple EDs. Same-sex peers and siblings with an ED were especially strong influences. “Rich” day-to-day networks of multiple social contacts with EDs were associated with multiple ED diagnoses. Media appeared to complement these social contacts. However, only dense networks of supportive others were significantly predictive of recovery. Effective ED treatment requires a careful consideration of social influences who may model ED attitudes and behaviours; same-sex siblings and peers are especially critical. ED treatment and recovery might be compromised if these significant others model and reinforce a patient’s ED attitudes and behaviours.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».