The rise of human service chains: antecedents to acquisitions and their effects on the quality of care in US nursing homes
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper studies acquisitions of nursing home facilities by chains. We first test alternative ‘cream‐skimming’ and ‘turn‐around’ arguments concerning nursing home acquisitions. We then consider post‐acquisition changes in nursing home health performance, differentiating effects of the acquisition process from those of prior strategy and performance of the acquired home and acquiring chain. Our dynamic empirical analysis of more than 5000 acquisitions by US nursing home chains from 1991 through 1997 shows that nursing home chain acquisitions are driven by a turn around logic, and that performance depends on the prior quality of the target and acquirer. Our analysis is relevant to policy on the nursing home sector, helping clarify why certain homes are acquired and how being acquired affects their residents' welfare. At a more general level, we offer insights concerning strategic factors that promote acquisition and drive expansion of service sector chains. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it