Resilience Informatics in Public Health: Qualitative Analysis of Conference Proceedings
Notice bibliographique
Résumé
BACKGROUND: In recent years, public health has confronted 2 formidable challenges: the devastating COVID-19 pandemic and the enduring threat of climate change. The convergence of these crises underscores the urgent need for resilient solutions. Resilience informatics (RI), an emerging discipline at the intersection of informatics and public health, leverages real-time data integration from health systems, environmental monitoring, and technological tools to develop adaptive responses to multifaceted crises. It offers promising avenues for mitigating and adapting to these challenges by proactively identifying vulnerabilities and fostering adaptive capacity in public health systems. Addressing critical questions regarding target audiences, privacy concerns, and scalability is paramount to fostering resilience in the face of evolving health threats. OBJECTIVE: The University of Arizona held a workshop, titled Resilience Informatics in Public Health, in November 2023 to serve as a pivotal forum for advancing these discussions and catalyzing collaborative efforts within the field. This paper aims to present a qualitative thematic analysis of the findings from this workshop. METHODS: A purposive sampling strategy was used to invite 40 experts by email from diverse fields, including public health, medicine, weather services, informatics, environmental science, and resilience, to participate in the workshop. The event featured presentations from key experts, followed by group discussions facilitated by experts. The attendees engaged in collaborative reflection and discussion on predetermined questions. Discussions were systematically recorded by University of Arizona students, and qualitative analysis was conducted. A detailed thematic analysis was performed using an inductive approach, supported by MAXQDA software to manage and organize data. Two independent researchers coded the transcripts; discrepancies in coding were resolved through consensus, ensuring a rigorous synthesis of the findings. RESULTS: The workshop hosted 27 experts at the University of Arizona, 21 (78%) of whom were from public health-related fields. Of these 27 experts, 8 (30%) were from the field of resilience. In addition, participants from governmental agencies, American Indian groups, weather services, and a mobile health organization attended. Qualitative analysis identified major themes, including the potential of RI tools, threats to resilience (eg, health care access, infrastructure, and climate change), challenges with RI tools (eg, usability, funding, and real-time response), and standards for RI tools (eg, technological, logistical, and sociological). The attendees emphasized the importance of equitable access, community engagement, and iterative development in RI projects. CONCLUSIONS: The RI workshop emphasized the necessity for accessible, user-friendly tools bridging technical knowledge and community needs. The workshop's conclusions provide a road map for future public health resilience, highlighting the need for scalable, culturally sensitive, community-driven interventions. Future directions include focused discussions to yield concrete outputs such as implementation guidelines and tool designs, reshaping public health strategies in the face of emerging threats.
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 | 0,057 | 0,153 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,011 | 0,017 |
| Études des sciences et des technologies | 0,012 | 0,007 |
| Communication savante | 0,011 | 0,007 |
| Science ouverte | 0,005 | 0,013 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
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 ».