Measurement of Nurse Job Satisfaction Using the McCloskey/Mueller Satisfaction Scale
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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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".