The Contribution of Alexithymia to Burnout in Forensic Physicians.
Bibliographic record
Abstract
The Aim: The aim of the study was to identify the relationship between the level of burnout subdomains and alexithymia among forensic physicians working in forensic institutions in Romania. Material and Methods: A number of 37 forensic physicians were included in the survey. Burnout and alexithymia were measured by using the Maslach Burnout Inventory and Toronto Alexithymia Scale. The obtained data were processed using the SPSS 17.00 statistical software. Results: The subjects obtained an average of 43.27±3.71, which corresponds to a low level of alexithymia. For burnout scores, we have obtained M=14.97±13.13 for emotional exhaustion, M=7.91±6.87 for depersonalization and M=33.18±10.59 for personal accomplishment (low-level for emotional exhaustion and medium-level burnout for the other two factors). Among the socio-demographic variables, only the age correlated positively with the burnout factor personal accomplishment. Positive correlations were identified between burnout factors and TAS-20. Comparative analysis results are important for the presence of insomnia, depression, teaching activity and looking for professional support after critical events. Conclusions: Scores for forensic physicians are low-level for emotional exhaustion and medium-level for two subdomains and low-level for alexithymia. Factors revealed by the comparative analysis are important to adjust professional activity and to find strategies to cope with stressful professional events.
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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 | 0.004 |
| 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 | 0.001 | 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".