The effect of nursing home closure on local employment in the United States
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of nursing home closure on local employment, overall, and by rurality. DATA SOURCES AND STUDY SETTING: We obtained 2008-2018 county-level data from the Quarterly Workforce Indicators, Medicare Provider of Services, Area Health Resource, and Urban Influence Codes files. From 2008 to 2018, 878 counties experienced at least one nursing home closure, and 2055 counties did not experience a closure. STUDY DESIGN: Using a difference-in-difference study design, we compare the changes of total employment, health sector employment and non-health sector employment over time between counties with and without a nursing home closure. We utilize the variation in the year and quarter of nursing home closures to estimate subsequent employment changes as well as employment trends before closure. We also account for contemporaneous events including nursing home entries and hospital entries and closures, and evaluate heterogeneity by rurality. DATA EXTRACTION: We include data on nursing home closure from the Medicare Provider of Service file. Quarterly county-level employee counts were obtained from the Quarterly Workforce Indicators provided by the Census Bureau. County-level demographic data were obtained from the Area Health Resource Files. We use Urban Influence Codes from the Economic Research Service, Department of Agriculture, to classify metropolitan, micropolitan, and rural (noncore) counties. PRINCIPAL FINDINGS: Health sector employment decreased by about 3.2%-4.1% (p < 0.01) in counties with a nursing home closure. The reduction was largest in rural counties (approximately 7.2%-9.4%, p < 0.01). The reduction in health sector employment persisted over time, particularly in rural counties. Overall, there was no discernable effect on non-health sector employment. CONCLUSIONS: Nursing home closure is associated with a persistent decline in health sector employment, particularly in rural counties, suggesting a reduction in the health care workforce and in the ability to sustain health care services supply, particularly in rural areas.
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".