Marketization in Long-Term Care: A Cross-Country Comparison of Large For-Profit Nursing Home Chains
Bibliographic record
Abstract
This article presents cross-country comparisons of trends in for-profit nursing home chains in Canada, Norway, Sweden, United Kingdom, and the United States. Using public and private industry reports, the study describes ownership, corporate strategies, costs, and quality of the 5 largest for-profit chains in each country. The findings show that large for-profit nursing home chains are increasingly owned by private equity investors, have had many ownership changes over time, and have complex organizational structures. Large for-profit nursing home chains increasingly dominate the market and their strategies include the separation of property from operations, diversification, the expansion to many locations, and the use of tax havens. Generally, the chains have large revenues with high profit margins with some documented quality problems. The lack of adequate public information about the ownership, costs, and quality of services provided by nursing home chains is problematic in all the countries. The marketization of nursing home care poses new challenges to governments in collecting and reporting information to control costs as well as to ensure quality and public accountability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".