Psychometric evaluation of the <scp>McC</scp>loskey/Mueller Satisfaction Scale
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.
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.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".