Measurement of Nurse Job Satisfaction Using the McCloskey/Mueller Satisfaction Scale
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.
Bibliographic record
Abstract
BACKGROUND: Originally developed to rank rewards that nurses value and that encourage them to remain in their jobs, the McCloskey/Mueller Satisfaction Scale (MMSS) is being used extensively in research and practice to measure nurse job satisfaction. Since its original development in 1990, limited evidence of psychometric properties of the MMSS has been reported. OBJECTIVE: To investigate and report the psychometric properties of the MMSS when used in 2003 to measure hospital nurse job satisfaction. METHODS: Data from a survey of 8,456 nurses were used to establish psychometric properties of the MMSS. Dimensionality was tested using confirmatory and exploratory factor analyses. Validity of new MMSS factors was tested by investigating relationships of the new factors with theoretically related concepts and by testing ability of the new factors to predict nurses' intentions to remain employed in their hospitals. Reliability coefficients of the new factors are reported. RESULTS: The original eight factors could not be replicated satisfactorily using confirmatory factor analysis. Exploratory factor analysis found a seven-factor model rather than the original eight factors previously reported. Validity of this new model was supported. However, similar to the original instrument, weak internal consistency reliability coefficients were found for three of the new MMSS factors. DISCUSSION: From a research perspective, using an instrument with 23 items that measure 7 aspects of nurse job satisfaction is more desirable than an instrument with 31 items. However, MMSS items must be redeveloped to improve internal consistency of factors.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it