Mapping Nursing Home Inspections & Audits in Six Countries
Bibliographic record
Abstract
International quality concerns regarding long-term residential care, home to many of the most vulnerable among us, prompted our examination of the audit and inspection processes in six different countries. Drawing on Donabedian’s (Evaluation & Health Professions, 6(3), 363–375, 1983) categorization of quality criteria into structural, process and outcome indicators, this paper compares how quality is understood and regulated in six countries occupying different categories according to Esping Andersen’s (1990) typology: Canada, England, and the United States (liberal welfare regimes); Germany (conservative welfare regime); Norway, and Sweden (social democratic welfare regimes). In general, our review finds that countries with higher rates of privatization (mostly the liberal welfare regimes) have more standardized, complex and deterrence-based regulatory approaches. We identify that even countries with the lowest rates of for profit ownership and more compliance-based regulatory approaches (Norway and Sweden) are witnessing an increased involvement of for-profit agencies in managing care in this sector. Our analysis suggests there is widespread concern about the incursion of market forces and logic into this sector, and about the persistent failure to regulate structural quality indicators, which in turn have important implications for process and outcome quality indicators.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".