Spiritual well-being and moral distress among Iranian nurses
Bibliographic record
Abstract
BACKGROUND: Moral distress is increasingly recognized as a problem affecting healthcare professionals, especially nurses. If not addressed, it may create job dissatisfaction, withdrawal from the moral dimensions of patient care, or even encourage one to leave the profession. Spiritual well-being is a concept which is considered when dealing with problems and stress relating to a variety of issues. OBJECTIVE: This research aimed to examine the relationship between spiritual well-being and moral distress among a sample of Iranian nurses and also to study the determinant factors of moral distress and spiritual well-being in nurses. RESEARCH DESIGN: A cross-sectional, correlational design was employed to collect data from 193 nurses using the Spiritual Well-Being Scale and the Moral Distress Scale-Revised. 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: = .462). Marital status and job satisfaction were found to be independent predictors of spiritual well-being. However, gender and educational levels were found to be independent predictors for moral distress. Age, working in rotation shifts, and a tendency to leave the current job also became significant after adjusting other factors for moral distress. DISCUSSION AND CONCLUSION: This study could not support the relationship between spiritual well-being and moral distress. However, the results showed that moral distress is related to many elements including individual ideals and differences as well as organizational factors. Informing nurses about moral distress and its consequences, establishing periodic consultations, and making some organizational arrangement may play an important role in the identification and management of moral distress and spiritual well-being.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".