“I Didn’t Know Him Before the Pandemic… Now He's on My Speed Dial”: Strengthening Collaboration Between Infectious Diseases Physicians and State and Local Public Health for Future Public Health Emergencies
Bibliographic record
Abstract
BACKGROUND: Infectious diseases (ID) physicians play a crucial role in public health emergencies. Effective collaboration between public health agencies and healthcare providers is essential for a coordinated response. However, there is limited information on how ID physicians and health departments collaborate and which areas need to be improved. Here, we identify ways to enhance public health preparedness through increased collaboration between ID physicians and state, tribal, local, and territorial health departments. METHODS: We performed a secondary qualitative analysis of 37 telephone interviews conducted using a semistructured discussion guide. Interviews were conducted from July 2023 through September 2023 as part of a pandemic preparedness needs assessment by the Infectious Diseases Society of America's COVID-19 Real-Time Learning Network. Participants included ID physicians (n = 13), public health workers (n = 7), healthcare facility-based pandemic leaders (n = 7), and national stakeholders (n = 10). RESULTS: While some jurisdictions had robust connections between ID physicians and public health staff, lack of coordination in other areas led to duplication of efforts, confusion, and underutilization of resources. Respondents indicated that collaboration can be strengthened over time. Recommendations included better data systems, standardized reporting procedures, early dissemination of updates, and training of ID physicians in the incident command structure and media communication. CONCLUSIONS: The opportunity to build on institutional knowledge from the coronavirus disease 2019 pandemic will be lost without a commitment of time, resources, and planning. Public health officials can use this experience as a catalyst for building strong collaborative relationships between ID physicians and public health practitioners, a cornerstone of future pandemic response.
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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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".