Exploratory analysis of the professional quality of life in an Italian radiotherapy department: The role of empathy and alexithymia
Bibliographic record
Abstract
Purpose: Professional quality of life (QoL) is crucial for healthcare workers as it affects performance at work and interaction with patients, but little is known about stressors influencing radiation oncology professionals. The present study aims to explore the professional QoL of radiation oncologists (ROs) and radiation therapists (RTTs) in an Italian radiotherapy department and to report data about the possible impact of personality factors, such as alexithymia and empathy. Material and methods: Participants filled out three validated questionnaires measuring the professional QoL, alexithymia, and empathy: (i) Professional Quality of Life Scale (ProQOL); (ii) Toronto Alexithymia Scale (TAS-20); (iii) Interpersonal Reactivity Index (IRI). Correlation, regression analyses and non-parametric tests were run. Results: A total of 48 professionals completed the survey (66.7% ROs, 33.3% RTTs). Considering the ProQOL dimensions, moderate levels of risk for burnout (BO) and secondary traumatic stress (STS) were found. BO was found to be predictive by TAS-20 total score (β=.37, p =.010), while STS resulted to be predictive by TAS-20 total score (β=.54, p <.001) and IRI Empathic Concern subscale (β=.45, p <.001). No significant differences were found between ROs and RTTs for all the considered variables, except for TAS-20 total score ( p =.032), higher for RTTs. Conclusions: Results showed no evidence of high risk of burnout and no intrinsic differences regarding the professional QoL between ROs and RTTs. Findings suggest a significant role of alexithymia and empathy predicting professional QoL. These results underscore the importance of promoting a positive work environment and emotional competencies to prevent high stress levels.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".