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Record W2104493971 · doi:10.1080/08959420.2015.1022102

Medicare and Medicaid Reimbursement Rates for Nursing Homes Motivate Select Culture Change Practices But Not Comprehensive Culture Change

2015· article· en· W2104493971 on OpenAlexaff
Michael Lepore, Renée R. Shield, Jessica Looze, Denise Tyler, Vincent Mor, Susan C. Miller

Bibliographic record

VenueJournal of Aging & Social Policy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsInstitute of Health Services and Policy Research
FundersNational Institute on AgingAgency for Healthcare Research and Quality
KeywordsMedicaidCulture changeOrganizational cultureReimbursementContext (archaeology)EmpowermentNursingNursing homesBusinessMedicinePublic relationsSociologyPolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Components of nursing home (NH) culture change include resident-centeredness, empowerment, and home likeness, but practices reflective of these components may be found in both traditional and "culture change" NHs. We use mixed methods to examine the presence of culture change practices in the context of an NH's payer sources. Qualitative data show how higher pay from Medicare versus Medicaid influences implementation of select culture change practices, and quantitative data show NHs with higher proportions of Medicare residents have significantly higher (measured) environmental culture change implementation. Findings indicate that heightened coordination of Medicare and Medicaid could influence NH implementation of reform practices.

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)0.007
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.196
GPT teacher head0.505
Teacher spread0.310 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2015
Admission routes1
Has abstractyes

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