MétaCan
Menu
Back to cohort
Record W3087163201 · doi:10.1093/geront/gnaa141

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

2020· review· en· W3087163201 on OpenAlexaff
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

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.007
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations50
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe GerontologistSame topicGeriatric Care and Nursing HomesFrench-language works237,207