Predicting Job Burnout of Medical Staff of Karaj Government Hospitals by Ego Strength: Mediating Role of Emotion Regulation and Alexithymia
Bibliographic record
Abstract
The objective of the current study was to examine the mediating role of alexithymia and emotion regulation in the prediction of job exhaustion among the treatment staff of Karaj city government hospitals, as determined by ego strength. The current research was descriptive of the correlation type. The research’s statistical population comprised all active medical personnel employed by Karaj public hospitals during the spring of 2024. 250 people (97 men and 153 women) were selected as a sample using the available sampling method. The tools of the current research included Ego Strength Scale (ESS), Emotion Regulation Questionnaire (ERQ), Toronto alexithymia Scale (TAS) and Maslach Job Burnout Questionnaire (MBI). The structural equation analysis method was employed to analyze the collected data in SPSS version 25 and Mplus version 8.3. The obtained findings indicated that job burnout was substantially and negatively predicted by Ego Strength. Ego Strength had a direct and significant effect on emotion regulation and alexithymia, and emotion regulation and alexithymia also had a direct and significant effect on job burnout. The indirect path analysis results indicated that the relationship between ego strength and job burnout is substantially mediated by emotion regulation and alexithymia. Considering the stressful environment of hospitals and considering the findings of the research, it is possible to design and implement educational and intervention programs based on ego strength with more emphasis on emotional dimensions, including emotion regulation and alexithymia, in order to reduce the burnout of medical staff.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".