The Exodus Of State And Local Public Health Employees: Separations Started Before And Continued Throughout COVID-19
Bibliographic record
Abstract
Understanding the size and composition of the state and local governmental public health workforce in the United States is critical for promoting and protecting the health of the public. Using pandemic-era data from the Public Health Workforce Interests and Needs Survey fielded in 2017 and 2021, this study compared intent to leave or retire in 2017 with actual separations through 2021 among state and local public health agency staff. We also examined how employee age, region, and intent to leave correlated with separations and considered the effect on the workforce if trends were to continue. In our analytic sample, nearly half of all employees in state and local public health agencies left between 2017 and 2021, a proportion that rose to three-quarters for those ages thirty-five and younger or with shorter tenures. If separation trends continue, by 2025 this would represent more than 100,000 staff leaving their organizations, or as much as half of the governmental public health workforce in total. Given the likelihood of increasing outbreaks and future global pandemics, strategies to improve recruitment and retention must be prioritized.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".