Reductions of Budgets, Staffing, and Programs Among Local Health Departments
Bibliographic record
Abstract
OBJECTIVES: To provide an overview of budget cuts, job losses, and program reductions among local health departments (LHDs) and to examine the association between LHD infrastructure characteristics and the likelihood of budget cuts. DESIGN: Data from 4 waves of the economic surveillance survey (July-August 2009, September-November 2010, January-February 2012, and January-March 2013) conducted by the National Association of County & City Health Officials were analyzed to assess cuts to budgets, jobs, and programs since 2009. Data from the 2013 National Profile of Local Health Departments survey were used to assess the infrastructural characteristics associated with budget cuts. RESULTS: When asked in early 2013, more than a quarter of LHDs (26.9%) reported a reduced budget, continuing the trend of a substantial proportion of LHDs experiencing financial hardship in recent years. The percentages of LHDs that made cuts to programmatic areas fluctuated from year to year but have never been lower than 40%. Maternal and child health services were among areas most often cut during all 4 time points of the survey. Governance type, total expenditures, and percentage of revenues from local sources were significantly associated with LHD budget cuts. CONCLUSIONS: Cuts in LHD budgets, staff, and activities have been widespread for a period that lasted long after the official end of the Great Recession. There is a great need for substantive and consistent funding to ensure the retention of the workforce and the delivery of essential public health services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".