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Record W3087163201 · doi:10.1093/geront/gnaa141

Medical Care Delivery in U.S. Nursing Homes: Current and Future Practice

2020· review· en· W3087163201 on OpenAlex
Paul R. Katz, Kira L. Ryskina, Debra Saliba, Andrew P. Costa, Hye‐Young Jung, Laura M. Wagner, Mark Aaron Unruh, Benjamin J. Smith, Andrea Moser, Joanne Spetz, Sid Feldman, Jurgis Karuza

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Gerontologist · 2020
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of TorontoMcMaster University
FundersNational Institute on AgingNational Institutes of Health
KeywordsWorkforceNursingContext (archaeology)Perspective (graphical)Health careHealth care deliveryQuality (philosophy)MedicineNurse practitionersBusinessPolitical science

Abstract

fetched live from OpenAlex

The delivery of medical care services in U.S. nursing homes (NHs) is dependent on a workforce that comprises physicians, nurse practitioners, and physician assistants. Each of these disciplines operates under a unique regulatory framework while adhering to common standards of care. NH provider characteristics and their roles in NH care can illuminate potential links to clinical outcomes and overall quality of care with important policy and cost implications. This perspective provides an overview of what is currently known about medical provider practice in NH and organizational models of practice. Links to quality, both conceptual and established, are presented as is a research and policy agenda that addresses the gaps in the evidence base within the context of our ever-changing health care landscape.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.489
Teacher spread0.405 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it