Rasch analysis of Stamps's Index of Work Satisfaction in nursing population
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".