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Record W2110637833 · doi:10.1002/nur.10043

Measuring the hospital practice environment: A Canadian context

2002· article· en· W2110637833 on OpenAlexaffabout
Carole A. Estabrooks, Ann E. Tourangeau, Charles Humphrey, Kathryn L. Hesketh, Phyllis Giovannetti, Donna Thomson, Jennifer Wong, Sonia Acorn, Heather Clarke, Judith Shamian

Bibliographic record

VenueResearch in Nursing & Health · 2002
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsHealth CanadaUniversity of British ColumbiaUniversity of TorontoCentre for Advancing Health OutcomesInstitute for Clinical Evaluative SciencesUniversity of Alberta
FundersNational Institute of Nursing Research
KeywordsContext (archaeology)Exploratory factor analysisIndex (typography)NursingWork (physics)Work environmentSample (material)MedicineAcute careRelevance (law)PsychologyPsychometricsHealth careJob satisfactionSocial psychologyPolitical scienceGeographyComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

The primary purpose of this study is to document the psychometric properties of the revised Nursing Work Index (NWI-R) in the context of a large Canadian sample of registered nurses. A self-administered survey containing the NWI-R was completed by 17,965 registered nurses working in 415 hospitals in three Canadian provinces. Using exploratory principal components analysis, with a forced one-factor solution, the practice environment index was obtained. In addition, key assumptions were tested from previous work about the rationale for the aggregation of NWI-R responses. In the Canadian context the one-factor solution provides a parsimonious index of the practice environment of registered nurses working in acute care hospitals. Further work is needed to determine the predictive capability of this index and its relevance to cross-national organizational contexts.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.084
Threshold uncertainty score0.609

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0100.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.120
GPT teacher head0.413
Teacher spread0.293 · 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

Citations137
Published2002
Admission routes2
Has abstractyes

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