The relationship between alexithymia and burnout in nurses: A cross-sectional study
Bibliographic record
Abstract
This study evaluated the relationship between alexithymia and job burnout in nurses, providing practical insights for healthcare professionals. The present research employed a cross-sectional design to examine this relationship among nurses employed at medical training centers in Rasht City during 2021-2022. The data collection instruments utilized in this study included the personal and occupational profile questionnaire, the Toronto alexithymia scale, and the Maslach burnout inventory. A multi-stage sampling method was employed proportionate to the population size, and ultimately, convenience sampling was utilized. A total of 258 nurses participated in this research. The study found that the average age of the nurses is 35.99 (standard deviation [SD]=8.19) years, with an average work experience of 11.78 (SD=7.40) years. The mean total alexithymia score among the nurses studied was 51.2 (SD=9.6). The average score for the emotional exhaustion dimension was 20.2 (SD=12.3), the average score for the depersonalization dimension was 3.5 (SD=6.3), and the average score for the personal accomplishment dimension was 32.0 (SD=10.2). The study also revealed a weak but statistically significant positive correlation between the total alexithymia and emotional exhaustion scores (r=0.272, P<0.001). A moderately significant positive correlation was also observed between the total alexithymia and depersonalization scores (r=0.441, P<0.001). A moderately significant negative correlation was found between the total alexithymia score and the personal accomplishment score among the nurses studied (r=0.345, P<0.001). In conclusion, the study calls for nursing managers and policymakers to prioritize mental health support and training programs tailored to the needs of nurses, thereby empowering them to enhance their well-being and resilience and ultimately leading to better patient outcomes and a more resilient healthcare workforce.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".