A Psychometric Analysis of the Nurse Satisfaction with the Quality of Care Scale
Bibliographic record
Abstract
The concept of quality of nursing care can vary across healthcare organizations, and many different factors may affect the quality of nursing care as perceived by nurses. Measuring satisfaction with quality of nursing care from the nurse's perspective is important as a valid and reliable indicator of care quality. The purpose of this study was to measure the psychometric properties of a researcher-developed instrument measuring nurse satisfaction with quality of care. A sample of 200 nurses was randomly selected from three different cities in Saudi Arabia and surveyed with the Nurse Satisfaction with Quality of Care Scale, which is a self-administrated five-item scale. Exploratory factor analysis, confirmatory factor analysis, and internal consistency analysis were conducted to assess aspects of the validity and reliability of the instrument. The results of exploratory factor analysis supported a one-factor structure that consisted of the five items. Confirmatory factor analysis results confirmed that the five items were integral to nurse satisfaction with quality of care. The Cronbach internal consistency of the scale was acceptable. The scale appeared to be a reliable and valid tool for assessing nurse perceptions of their satisfaction with the quality of care provided. Additional studies to further test psychometric properties of this scale in different contexts are warranted.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".