BURNOUT, ALEXITHYMIA AND JOB SATISFACTION IN AUTOPSY TECHNICIANS
Bibliographic record
Abstract
Aim. Identify the presence of burnout syndrome among autopsy technicians working in Pathology and Forensic Pathology departments whilst relating the level of burnout subdomains with job satisfaction and alexithymia. Material and methods. A self-administered questionnaire was created specifically for this research collecting socio- demographic data and job-related information. Also, three psychological instruments were applied to evaluate burnout syndrome, satisfaction with work and alexithymia: Maslach Burnout Inventory (MBI), Job Satisfaction Scale and Toronto Alexithymia Scale (TAS). Statistical analysis of data was performed using Statistical Package for Social Sciences (SPSS) version 21. Results. A number of 26 autopsy technicians were included in the study with a mean (M) work experience of 16.39 ± 11.33 years. Low levels of burnout were identified for two of the subdomains emotional exhaustion and depersonalization; 28.6% of participants had low scores for personal-accomplishment subscale, 14.3% had moderate scores, and 57.1% had high scores and displayed high levels of job satisfaction. Most participants (77.3%) did not have alexithymia. Subjects who were confronted with critical events scored higher on alexithymia. 80% of the subjects consider that working with child victims disturb them the most. Subjects who faced events with a high emotional impact had higher scores on alexithymia compared to those who did not (M=33.20): t(20)=2.426, p=.025. Conclusions. Facing events with children as victims determine autopsy technicians to be more prone to present higher scores on TAS. None of the participants contacted a specialist to face critical events or to find coping strategies coordinated by a specialist in case of job-related stress. Results related to alexithymia are important for both medical professionals and trainers to focus on the impact of various job-related events on alexithymia.
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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 | 0.001 |
| 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) | 0.002 | 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".