Burnout in Pediatric Oncology: Team Building and Clay Therapy as a Strategy to Improve Emotional Climate and Group Dynamics in a Nursing Staff
Bibliographic record
Abstract
Healthcare professionals in pediatric oncology are at a high risk of burnout. Art therapy is being increasingly recognized as a potential tool for reducing stress and improving emotional well-being. The Art-Out pilot project aimed at nursing staff was initiated in a pediatric oncology unit. The staff members participating in the project were guided in a team-building course integrated with art and clay therapy, aiming to reduce burnout levels, improve emotional climate, and strengthen resilience. METHODS: Burnout levels were assessed through the Maslach Burnout Inventory (MBI), alexithymia was measured with the Toronto Alexithymia Scale (TAS-20), and emotional regulation difficulties were evaluated through the Difficulties in Emotion Regulation Scale (DERS); these tests were assessed before (T0) and after (T1) the team-building course (Art-Out project). RESULTS: Data analysis showed a significant reduction in burnout, alexithymia, and emotional dysregulation, highlighting the positive impact of this approach in improving team dynamics and emotional management. CONCLUSIONS: Our study confirms the high risk of burnout, alexithymia, and emotional dysregulation among pediatric oncology healthcare workers, underscoring the need for targeted interventions to prevent and mitigate these risks.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".