Supporting and Incentivizing Peer Leaders for an Internet-Based Private Peer Community for Youths With Type 1 Diabetes: Social Network and Directed Content Analysis
Notice bibliographique
Résumé
BACKGROUND: Youths with type 1 diabetes (T1D) frequently experience stigma. Internet-based peer communities can mitigate this through social support but require leaders to catalyze exchange. Whether nurturing potential leaders translates into a central role has not been well studied. Another issue understudied in such communities is lurking, the viewing of exchanges without commenting or posting. OBJECTIVE: We aimed to assess the centrality of the peer leaders we selected, trained, and incentivized within the Canadian Virtual Peer Network (VPN)-T1D. This is a private Facebook (Meta Platforms, Inc) group that we created for persons aged 14 to 24 years with T1D. We specifically sought to (1) compare a quantitative estimate of network centrality between peer leaders and regular members, (2) assess the proportions of network exchanges that were social support oriented, and (3) assess proportions of high engagement (posts, comments, reactions, and votes) and low engagement (lurking) exchanges. METHODS: We recruited peer leaders and members with T1D from prior study cohorts and clinics. We trained 10 leaders, provided them with a monthly stipend, and encouraged them to post on the private Facebook group we launched on June 21, 2017. We extracted all communications (posts, messages, reactions, polls, votes, and views) that occurred until March 20, 2020. We calculated each member's centrality (80% of higher engagement communications comprising posts, comments, and reactions plus 20% of members with whom they connected). We divided each member's centrality by the highest centrality to compute the relative centrality, and compared the mean values between leaders and members (linear regression). We calculated the proportions of communications that were posts, comments, reactions, and views without reaction. We performed content analysis with a social support framework (informational, emotional, esteem-related, network, and tangible support), applying a maximum of 3 codes per communication. RESULTS: VPN-T1D gained 212 regular members and 10 peer leaders over 33 months; of these 222 members, 26 (11.7%) exited. Peer leaders had 10-fold higher relative centrality than regular members (mean 0.53, SD 0.26 vs mean 0.04, SD 0.05; 0.49 difference; 95% CI 0.44-0.53). Overall, 91.4% (203/222) of the members connected at least once through posts, comments, or reactions. Among the 75,051 communications, there were 5109 (6.81%) posts, comments, and polls, 6233 (8.31%) reactions, and 63,709 (84.9%) views (lurking). Moreover, 54.9% (3430/6253) of codes applied were social support related, 66.4% (2277/3430) of which were informational (eg, insurance and travel preparation), and 20.4% (699/3430) of which were esteem related (eg, relieving blame). CONCLUSIONS: Designating, training, and incentivizing peer leaders may stimulate content exchange and creation. Social support was a key VPN-T1D deliverable. Although lurking accounted for a high proportion of the overall activity, even those demonstrating this type of passive participation likely derived benefits, given that the network exit rate was low. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18714.
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 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».