Testing the Correlation Model of Psychological Well-Being, Self-Compassion, and Mindfulness in Married Nurses with the Mediating Role of Alexithymia
Bibliographic record
Abstract
Throughout married life, various factors affect communication, mental health, and quality of life. Understanding these factors is foundational to strengthening family life. This study aimed to test the correlation model of psychological well-being, self-compassion, and mindfulness in married nurses with the mediating role of alexithymia. The present research employed a descriptive-correlational method. The statistical population included all married nurses in Ilam County during the first half of 2023. The sample comprised 240 nurses selected through convenience sampling. Data collection tools included a demographic questionnaire, the Psychological Well-Being Scale, the Self-Compassion Scale, the Freiburg Mindfulness Inventory, and the Toronto Alexithymia Scale. Data analysis was performed using Amos software version 24. Structural equation modeling (SEM) was applied to evaluate the proposed model, and the bootstrapping method was used to test indirect relationships. The findings revealed that self-compassion and mindfulness could enhance psychological well-being in nurses by reducing alexithymia. It is recommended that the Ministry of Health and Medical Education organizes effective and constructive workshops and seminars focusing on realistic expectations in marital relationships, perceptions and motivations, mindful approaches, and strategies to enhance self-compassion to improve the psychological well-being of nurses
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".