Under-Funded and Under-Pressure: State Epidemiologists During the COVID-19 Response
Bibliographic record
Abstract
OBJECTIVES: We conducted interviews with state epidemiologists involved in the state-level COVID-19 response to understand the challenges and opportunities that state epidemiologists and state health departments faced during COVID-19 and consider the implications for future pandemic responses. METHODS: As part of a broader study on policymaking during COVID-19, we analyzed 12 qualitative interviews with state-epidemiologists from 11 US states regarding the challenges and opportunities they experienced during the COVID-19 response. RESULTS: Interviewees described the unprecedented demands COVID-19 placed on them, including increased workloads as well as political and public scrutiny. Decades of under-funding and constraints posed particular challenges for meeting these demands and compromised state responses. Emergency funding contributed to ameliorating some challenges. However, state health departments were unable to absorb the funds quickly, which created added pressure for employees. The emergency funding also did not resolve longstanding resource deficits. CONCLUSIONS: State health departments were not equipped to meet the demands of a comprehensive COVID-19 response, and increased funding failed to address shortfalls. Effective future pandemic responses will require sustained investment and adequate support to manage on-going and surge capacity needs. Increased public interest and skepticism complicated the COVID-19 response, and additional measures are needed to address these factors.
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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 | 0.042 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".