The Mediation Roles of Coping Modalities on the Relationship Between Stress and Quality of Life Among Jordanian Nurses
Bibliographic record
Abstract
Nurses are at the frontline, dealing with people's most immense healthcare needs in stressful and demanding work environments. Consequently, it is essential to thoroughly examine how various coping mechanisms might affect the relationship between stress and quality of life (QOL). This study aimed to examine the mediation effect of both problem-focused coping (PFC) and emotion-focused coping (EFC) mechanisms on mitigating the effect of stress on the QOL among Jordanian nurses. A multisite cross-sectional descriptive correlational design was used in this study. An online survey was completed by 203 nurses using a convenience sampling technique between October 2023 and January 2024. The study included nurses working in different Jordanian healthcare sectors including governmental, private, and university-affiliated hospitals. Several measures were used to collect data, including questionnaires on sociodemographics, QOL, coping, and stress. Two models were hypothesized for this study. The two models were analyzed using Andrew Hayes Process Macro Model 4 for testing the mediation effects. Additionally, descriptive and correlational analyses were run prior to the main analysis. The results showed that coping significantly mediated the relationship between stress and QOL with variations between PFC and EFC. In conclusion, psychological distress symptoms were common among Jordanian nurses; psychological distress, coping, and QOL are correlating variables. Nurses' stress levels and coping modalities can predict QOL with a superior effect of PFC compared with EFC. Strategies should be put in place to improve effective coping to improve nurses' QOL. The results of this study have important implications for nursing education, practice, future research, and policy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".