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Record W2917306641 · doi:10.1177/1178632919827926

An Overview of International Staff Time Measurement Validation Studies of the RUG-III Case-mix System

2019· review· en· W2917306641 on OpenAlex
Luke Turcotte, Jeff Poss, Brant E. Fries, John P. Hirdes

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

VenueHealth Services Insights · 2019
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCase mix indexReimbursementProspective payment systemPaymentResource consumptionPayment systemSkill mixResource (disambiguation)Health careVariety (cybernetics)MedicineResource useBusinessNursingComputer scienceEnvironmental resource managementFinanceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

resource consumption and may be used as the basis for prospective payment systems to ensure that facility reimbursement is commensurate with patient acuity. Since RUG-III's development in 1994, more than a dozen international staff time measurement studies have been published to evaluate the utility of the case-mix system in a variety of diverse health care environments around the world. This overview of the literature summarizes the results of these RUG-III validation studies and compares the performance of the algorithm across countries, patient populations, and health care environments. Limitations of the RUG-III validation literature are discussed for the benefit of health system administrators who are considering implementing RUG-III and next-generation resource utilization group case-mix systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.302
GPT teacher head0.507
Teacher spread0.205 · 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