An International Mapping of Medical Care in Nursing Homes
Bibliographic record
Abstract
Nursing home (NH) residents are increasingly in need of timely and frequent medical care, presupposing not only available but perhaps also continual medical care provision in NHs. The provision of this medical care is organized differently both within and across countries, which may in turn profoundly affect the overall quality of care provided to NH residents. Data were collected from official legislations and regulations, academic publications, and statistical databases. Based on this set of data, we describe and compare the policies and practices guiding how medical care is provided across Canada (2 provinces), Germany, Norway, and the United States. Our findings disclose that there is a considerable difference to find among jurisdictions regarding specificity and scope of regulations regarding medical care in NHs. Based on our data, we construct 2 general models of medical care: (1) more regulations-fee-for-service payment-open staffing models and (2) less regulation-salaried positions-closed staffing models. Some evidence indicates that model 1 can lead to less available medical care provision and to medical care provision being less integrated into the overall care services. As such, we argue that the service models discussed can significantly influence continuity of medical care in NH.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".