Investigating correlation between Alexithymia and Demographic variables with job burnout among nurses
Bibliographic record
Abstract
Introduction: The aim of this study was to investigate the correlation between alexithymia and demographic variables with job burnout among nurses. Methods: This descriptive-correlation study was conducted on 190 nursing students (78 males and 112 females) from nursing students studying in city of Tehran universities in 2009. The students were randomly selected and they answered the Tedim job burnout inventory and Toronto alexithymia Scale-20 (TAS-20). Descriptive statistics methods, Pearson correlation coefficient and regression analysis were used for statistical analysis. Results: Results of this study showed that job burnout among demographic variables with age and level of education had a positive and significant correlation. It showed that socioeconomic status is negatively correlated with burnout (r=-0.23, P<0.05). Alexithymia with low level of job burnout (r=0.38, P<0.001) and high level of job burnout had a negative and significant correlation (r=0.33, P<0.001). Regression analysis showed job burnout by age, level of education, socioeconomic status, difficulties in identifying feelings, difficulties describing feelings and externally-oriented thinking (P=0.001). Conclusion: Alexithymia and demographic variables are important factors in job burnout and these factors can account for a high amount of variance in job burnout. Knowledge about the relationship between alexithymia and demographic variables and job burnout could help nurses towards protecting and promoting mental health.
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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 | 0.002 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".