A mixed methods exploratory evaluation of burnout in frontline staff implementing dialectical behavior therapy on a pediatric eating disorders unit
Bibliographic record
Abstract
BACKGROUND: Eating disorders are life-threatening illnesses that commonly affect adolescents. The treatment of individuals with eating disorders can involve slow treatment progression and addressing comorbidities which can contribute to staff burnout. Dialectical behavior therapy (DBT) has emerged as a viable treatment option and has reduced staff burnout in several other settings. Our aim was to describe frontline staff burnout using mixed methodology on a DBT-trained combined inpatient/day hospital unit for pediatric eating disorders. METHOD: Frontline staff were trained to provide DBT skills for adolescents with eating disorders. Twelve months following the training and implementation, they completed the Copenhagen Burnout Inventory (CBI) and a qualitative interview. Directed and summative content analyses were used. RESULTS: Eleven frontline staff including nurses, child life specialists and child and youth workers participated. The CBI revealed that only one staff member experienced high personal burnout, while another experienced high client-related burnout. Qualitative data indicated that all frontline staff felt DBT had the potential to reduce burnout. CONCLUSION: Qualitative data indicate that staff believe that DBT may hold promise in reducing burnout for pediatric frontline staff who treat children and adolescents with eating disorders. Further study is needed. Understanding burnout is particularly important for nursing staff in inpatient and day hospital settings for eating disorders, as nursing staff generally have the most frequent patient contact; thought to be a risk factor for burnout. The reduction of burnout can prevent detrimental effects on job performance, personal well-being, and patient outcomes. Our exploratory study shows that frontline staff believe that DBT may have the potential to reduce burnout in staff treating children and adolescents with eating disorders in a combined inpatient/day hospital setting. Further study is needed in this area.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".