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Record W2478766696 · doi:10.1002/nop2.61

Rasch analysis of Stamps's Index of Work Satisfaction in nursing population

2016· article· en· W2478766696 on OpenAlex
Nora Ahmad, Nelson Ositadimma Oranye, Alyona Danilov

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

VenueNursing Open · 2016
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of ManitobaBrandon University
Fundersnot available
KeywordsRasch modelReliability (semiconductor)Index (typography)PsychologyJob satisfactionSample (material)Item response theoryApplied psychologyWork (physics)Polytomous Rasch modelPopulationStatisticsPsychometricsNursingClinical psychologySocial psychologyMedicineComputer scienceMathematicsEnvironmental healthEngineeringDevelopmental psychology

Abstract

fetched live from OpenAlex

AIM: One of the most commonly used tools for measuring job satisfaction in nursing is the Stamps Index of Work Satisfaction. Several studies have reported on the reliability of the Stamps' tool based on traditional statistical model. The aim of this study was to apply the Rasch model to examine the adequacy of Stamps's Index of Work Satisfaction for measuring nurses' job satisfaction cross-culturally and to determine the validity and reliability of the instrument using the Rasch criteria. DESIGN: A secondary data analysis was conducted on a sample of 556 registered nurses from two countries. METHODS: The RUMM 2030 software was used to analyse the psychometric properties of the Index of Work Satisfaction. RESULTS: The persons mean location of -0.018 approximated the items mean of 0.00, suggesting a good alignment of the measure and the traits being measured. However, at the items level, some items were misfiting to the Rasch model.

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)0.000
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 score0.620
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.369
Teacher spread0.343 · 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