Is Burn-Out the Main Issue? Impact of Aggressiveness and Depression in Burn-Out Among Operators in Oncology
Bibliographic record
Abstract
Over the last 30 years, Burn-out Syndrome has been mainly matched to psychological discomfort among the staff in Oncology Departments, therefore, it has been considered as the unique issue to be diagnosed, treated, and prevented.Our study is aimed to evaluate the relation between psychological states and traits with different Burn-out Scales from a clinical point of view.Three questionnaires: Link Burn-out Questionnaire (LBQ), State-Trait Anger Expression Inventory-2 (STAXI-2), and Beck Depression Inventory (BDI) have been given to the working staff including Medical Oncologists, Specialist Registrant, Nurses, and Healthcare Assistants at the Oncology Department from February to April 2016.Seventy-two operators have been included in our analysis.Male/Female (M/F) ratio was 18/54.Median age was 37 years old (range from 20 years old to 62 years old).We did not show a significant correlation between depression and operators' age, years of work, professional role, and relational decline, while a statistically significant association was observed between depression and professional ineffectiveness (p=0.042),disillusion (p=0.0003), and psychophysical exhaustion (p = 0.00001).According to STAXI 2, 15% of the personnel had a high expression of aggressiveness while 10% was over-controlled.Aggressiveness was statistically related to depression (p = 0.001), disillusion (p = 0.009), and psychophysical exhaustion (p = 0.012).This study showed that aggressiveness and depression play a crucial role in psychological discomfort in an Oncology Department operators.Therefore, they should be taken into account together with burn-out when performing screening procedures for the psychological discomfort in the same setting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".