The Relationship Between Death Anxiety and Alexithymia in Emergency Medical Technicians
Bibliographic record
Abstract
INTRODUCTION: When confronted with traumatic accidents and events that result in death, people are at risk of developing death anxiety. Due to their stressful job, emergency medical technicians (EMTs) will develop alexithymia and be unable to express and manage their emotions over time. Studies show that alexithymia causes physical and mental disorders in many people. The present study aimed to determine the relationship between death anxiety and alexithymia in EMTs. METHODS: The convenience sampling method was used to select 400 EMTs in southeastern Iran who met the inclusion criteria for this descriptive-analytical study. The Templer Death Anxiety Scale and the Toronto Alexithymia Scale were used to collect data. SPSS version 20 was used to analyze the data, which included descriptive and analytical statistics (Independent t test, ANOVA, Pearson correlation, and regression). RESULTS: The results of the study showed that the mean score of death anxiety in EMTs was 10.26 ± 3.69. It was revealed that 46.7% of the EMTs experienced severe death anxiety. Furthermore, the total mean score of alexithymia in EMTs was 59.65 ± 8.28, indicating the possibility of alexithymia. The Pearson correlation test showed a direct moderate relationship between death anxiety and alexithymia scores (r = .351, p < .001). CONCLUSION: According to the results, there is a direct significant relationship between death anxiety and alexithymia in EMTs. Therefore, it is suggested that EMTs be continuously taught effective methods to deal with death anxiety and reduce the physical and mental disorders caused by this problem.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".