Voluntary Separations and Workforce Planning: How Intent to Leave Public Health Agencies Manifests in Actual Departure in the United States
Bibliographic record
Abstract
OBJECTIVES: To ascertain levels of turnover in public health staff between 2014 and 2017 due to retirement or quitting and to project levels of turnover for the whole of the state and local governmental public health in the United States nationally. DESIGN: Turnover outcomes were analyzed for 15 128 staff from public health agencies between 2014 and 2017. Determinants of turnover were assessed using a logit model, associated with actually leaving one's organization. A microsimulation model was used to project expected turnover onto the broader workforce. RESULTS: Between 2014 and 2017, 33% of staff left their agency. Half of the staff who indicated they were considering leaving in 2014 had done so by 2017, as did a quarter of the staff who had said they were not considering leaving. Staff younger than 30 years constituted 6% of the workforce but 13% of those who left (P < .001). CONCLUSIONS: Public health agencies are expected to experience turnover in 60 000 of 200 000 staff positions between 2017 and 2020. IMPLICATIONS: As much as one-third of the US public health workforce is expected to leave in the coming years. Retention efforts, especially around younger staff, must be a priority. Succession planning for those retiring is also a significant concern.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".