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Record W1953269122 · doi:10.1080/08959420.2012.705696

Staffing-Related Deficiency Citations in Nursing Homes

2012· article· en· W1953269122 on OpenAlex
Shawna M. McDonald, Laura M. Wagner, Nicholas G. Castle

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

VenueJournal of Aging & Social Policy · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStaffingNursing homesCertificationNursingMultinomial logistic regressionMedicineBusinessLogistic regressionLong-term careQuality (philosophy)

Abstract

fetched live from OpenAlex

There is evidence that staffing characteristics influence quality of care in nursing homes. Federal and state surveyors conduct inspections of homes to assess their compliance with regulatory standards, including requirements related to staffing. Deficiency citations are issued when these standards are not met. This article examines the relationship between operational, facility, and market characteristics and organizational performance measured as staffing-related deficiency citations. Online Survey Certification of Automated Records (OSCAR) data from 2000 through 2007 were used with multinomial logistic regression analyses to identify factors associated with deficiency citations for staffing. Chain members and facilities with poor quality of care were more likely to receive deficiency citations for staffing. Greater bed count and competition between nursing homes were associated with a decreased likelihood of deficiency citations for staffing. Staffing-related deficiencies within nursing homes vary according to various operational, facility, and market characteristics.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.448
Teacher spread0.411 · 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