Psychometric evaluation of the Moral Distress Scale–Revised among Iranian Nurses
Bibliographic record
Abstract
BACKGROUND: Experiencing moral distress is traumatic for nurses. Ignoring moral distress can lead to job dissatisfaction, improper handling in the care of patients, or even leaving the job. Thus, it is crucial to use valid and reliable instruments to measure moral distress. OBJECTIVE: The purpose of this study was to determine the reliability and the validity of the Persian version of the Moral Distress Scale-Revised among a sample of Iranian nurses. RESEARCH DESIGN: In this methodological study, 310 nurses were recruited from all hospitals affiliated with the Qazvin University of Medical Sciences from February 2014 to April 2015. Data were collected using a demographic questionnaire and the Moral Distress Scale-Revised. The construct validity of the Moral Distress Scale-Revised was evaluated using principal component analysis and confirmatory factor analysis. Internal consistency reliability was assessed with Cronbach's alpha. ETHICAL CONSIDERATIONS: This study was approved by the Regional Committee of Medical Research Ethics. The ethical principles of voluntary participation, anonymity, and confidentiality were considered. FINDINGS: = 1.899, goodness-of-fit index = .904, comparative fit index = .927, incremental fit index = .929, and root mean square error of approximation (90% confidence interval) = .049 (.040-.057)) with all factor loadings greater than .5 and statistically significant. Cronbach's alpha coefficients were .853, .686, .685, and .711for the four factors. Moreover, the model structure was invariant across different income groups. DISCUSSION AND CONCLUSION: The Persian version of the Moral Distress Scale-Revised demonstrated suitable validity and reliability among nurses. The factor analysis also revealed that the Moral Distress Scale-Revised has a multidimensional structure. Regarding the proper psychometric characteristics, the validated scale can be used to further research about moral distress in this population.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".