Advancing community-engaged research during the COVID-19 pandemic: Insights from a social network analysis of the trans-LINK Network
Notice bibliographique
Résumé
Collaboration across sectors is critical to address complex health problems, particularly during the current COVID-19 pandemic. We examined the ability to collaborate during the pandemic as part of a baseline evaluation of an intersectoral network of healthcare and community organizations established to improve the collective response to transgender (trans) persons who have been sexually assaulted (the trans-LINK Network). A validated social network analysis survey was sent to 119 member organizations in Ontario, Canada. Survey respondents were asked, 'Has COVID-19 negatively affected your organization's ability to collaborate with other organizations on the support of trans survivors of sexual assault?' and 'How has COVID-19 negatively affected your organization's ability to collaborate within the trans-LINK Network?'. Data were analyzed using descriptive statistics. Seventy-eight member organizations participated in the survey (response rate = 66%). Most organizations (79%) indicated that the pandemic had affected their ability to collaborate with others in the network, citing most commonly, increased workload (77%), increased demand for services (57%), and technical and digital challenges (50%). Survey findings were shared in a stakeholder consultation with 22 representatives of 21 network member organizations. Stakeholders provided suggestions to prevent and address the challenges, barriers, and disruptions in serving trans survivors experienced during the pandemic, which were organized into themes. Seven themes were generated and used as a scaffold for the development of recommendations to advance the network, including: increase communication and knowledge exchange among member organizations through the establishment of a network discussion forum and capacity building group workshops; enhance awareness of network organizations by developing a member-facing directory of member services, their contributions, and ability to provide specific supports; strengthen capacity to provide virtual and in-person services and programs through enhanced IT support and increased opportunities for knowledge sharing and skill development; and adopt a network wide syndemic approach that addresses co-occurring epidemics (COVID-19 + racism, housing insecurity, transphobia, xenophobia) that impact trans survivors of sexual assault.
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 | 0,027 | 0,031 |
| 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 | 0,012 | 0,011 |
| Communication savante | 0,010 | 0,009 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».