Psychometric evaluation of the <scp>McC</scp>loskey/Mueller Satisfaction Scale
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Bibliographic record
Abstract
AIM: The aim of the study was to evaluate and refine the eight-factor structure of the 31 item McCloskey/Mueller Satisfaction Scale, which is one of the most widely used scales for measuring job satisfaction among nurses. However, this scale was developed in 1990 for the American nursing context and its psychometric validity and utility for use with non-American nurse populations have been questioned by various researchers. BACKGROUND: The eight-factor, 31-item McCloskey/Mueller Satisfaction Scale is one of the most widely used scales for measuring job satisfaction among nurses. However, this scale was developed in 1990 for the American nursing context, and its psychometric validity and utility for use with non-American nurse populations have been questioned by various researchers. METHODS: Data from a sample of 1007 Canadian nurses who were working in hospital and community settings were analyzed by using an exploratory factor analysis with varimax rotation. RESULTS: The original factor structure of the McCloskey/Mueller Satisfaction Scale was unable to be replicated. The best-fitting model that was obtained was a five-factor model with 25 items. The Cronbach's alphas for the new McCloskey/Mueller Satisfaction Scale subscales ranged from 0.71 to 0.87, which indicated stronger internal consistency than the original subscales (α = 0.52-0.84). CONCLUSION: The reliability and structural validity of the revised 25 item instrument suggest that it is a potentially sound tool for measuring nurses' job satisfaction. As a result of its sound dimensionality, it could be particularly useful when investigating individual and work factors that impact nurse job satisfaction or when evaluating the outcomes of organizational interventions that are aimed at increasing job satisfaction.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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