Resilience Informatics in Public Health: Qualitative Analysis of Conference Proceedings
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".