Connect, collaborate and tailor: a model of community engagement through infographic design during the COVID-19 pandemic
Notice bibliographique
Résumé
Abstract Background Across the globe, racial and ethnic minorities have been disproportionately affected by COVID-19 with increased risk of infection and burden from disease. Vaccine hesitancy has contributed to variation in vaccine uptake and compromised population-based vaccination programs in many countries. Connect, Collaborate and Tailor (CCT) is a Public Health Agency of Canada funded project to make new connections between public health, healthcare professionals and underserved communities in order to create culturally adapted communication about COVID-19 vaccines. This paper describes the CCT process and outcomes as a community engagement model that identified information gaps and created tailored tools to address misinformation and improve vaccine acceptance. Methods Semi-structured interviews with CCT participants were undertaken to evaluate the effectiveness of CCT in identifying and addressing topics of concern to underserved and ethnic minority communities. Interviews also explored CCT participants’ experiences of collaboration through the development of new partnerships between ethnic minority communities, public health and academic researchers, and the evolution of co-operation sharing ideas and creating infographics. Thematic analysis was used to produce representative themes. The activities described were aligned with the levels of public engagement described in the IAP2 spectrum (International Association for Public Participation). Results Analysis of interviews (n = 14) revealed that shared purpose and urgency in responding to the COVID-19 pandemic motivated co-operation among CCT participants. Acknowledgement of past harm, present health, and impact of social inequities on public service access was an essential first step in establishing trust. Creating safe spaces for open dialogue led to successful, iterative cycles of consultation and feedback between participants; a process that not only helped create tailored infographics but also deepened engagement and collaboration. Over time, the infographic material development was increasingly directed by community representatives’ commentary on their groups’ real-time needs and communication preferences. This feedback noticeably guided the choice, style, and presentation of infographic content while also directing dissemination strategies and vaccine confidence building activities. Conclusions The CCT process to create COVID-19 vaccine communication materials led to evolving co-operation between groups who had not routinely worked together before; strong community engagement was a key driver of change. Ensuring a respectful environment for open dialogue and visibly using feedback to create information products provided a foundation for building relationships. Finally, our data indicate participants sought reinforcement of close cooperative ties and continued investment in shared responsibility for community partnership-based public health.
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 | 0,025 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,013 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».