Help-Seeking, Support, and Engagement in Gestational Diabetes Mellitus Online Communities on Facebook: Content Analysis
Notice bibliographique
Résumé
BACKGROUND: The prevalence of gestational diabetes mellitus (GDM) has drastically risen in recent years. For some, self-management includes the use of GDM online communities on Facebook. Such communities can fill gaps in information and support that participants are not able to access elsewhere to address unmet needs. Given the popularity of sharing information about pregnancy on Facebook and the documented benefits of diabetes online communities, the same may be true of GDM online communities. OBJECTIVE: This study aimed to categorize and quantify what is being discussed in GDM Facebook groups, including informational and emotional help-seeking behavior, and how this support and engagement may be demonstrated by peers through comments and reactions. METHODS: We sourced the data from the 2 largest Facebook groups focused on GDM in Australia. A summative content analysis was conducted on original posts across the 2 groups and coded for topics as well as help-seeking types. The coding scheme was based on the previous work of Liang and Scammon. Visible indicators of engagement, including the number of comments and "reactions," were tabled and manually evaluated. RESULTS: There were 388 original posts, and the analysis produced 6 topics: GDM self-management (199/388, 51.3%), GDM clinical management (120/388, 30.9%), preparing for birth (40/388, 10.3%), mental distress (35/388, 9%), birth announcement (29/388, 7.5%), and GDM journey reflections (21/388, 5.4%). Secondary coding of help-seeking type revealed more than half of the posts were informational help-seeking (224/388, 57.7%), while a small proportion were both informational and emotional help-seeking (44/388, 11.3%), and some (12/388, 3.1%) were emotional help-seeking only. Self-disclosure was identified as a fourth category, comprising almost a quarter of all posts (90/388, 23.2%). A total of 6022 comments were posted in response to the original posts, and there were 4452 reactions across all posts. Emotional help-seeking attracted the most comments per thread (mean 21.5, SD 19.8), followed by informational and emotional help-seeking (mean 20.2, SD 14.7), informational help-seeking (mean 15.6, SD 14.6), and self-disclosure (mean 14.3, SD 21.8). Across all help-seeking categories, few reactions occurred compared to comments; in contrast, self-disclosure attracted a large number of reactions (mean 9.4, SD 45.3). CONCLUSIONS: This is one of the first studies to examine peer support in a GDM online community on Facebook. Our findings suggest that active participants' needs around information and support in relation to GDM are being somewhat met by peer-led online communities. Given the practical limitations of formal health care, including the provision of ongoing social support, it is important to recognize how GDM online communities can complement formal health care and help address unmet needs.
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 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,008 | 0,007 |
| 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 | 0,001 |
| 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 ».