The prevalence and solutions to burnout amongst surgical trainees
Bibliographic record
Abstract
Background: Burnout is a syndrome of emotional exhaustion, reduced sense of personal accomplishment and loss of sense of self. Healthcare workers in the UK are known to suffer high psychological distress and burnout. Increasing attrition rates among surgical trainees have also been noted, particularly among female trainees. However, there are limited data on the factors contributing to burnout potentially leading to trainee attrition. Interventions to combat burnout and improve trainee well-being are still in their infancy. This review reports burnout prevalence and methods implemented to reduce burnout and improve surgical trainee well-being. Objective: To report the prevalence and factors contributing to burnout and suggest evidence-based methods that reduce burnout and improve well-being within this cohort. Methods: A literature search was conducted across five databases, identifying papers on burnout prevalence among surgical trainees and reported gender. Papers outlining interventions to reduce burnout were also included. Papers were screened against our inclusion and exclusion criteria. Quality was assessed using the modified Newcastle–Ottawa Scale and data were extracted and presented in this review. Results: Following screening, 22 of 456 identified papers were included in the review; 11 papers were examined for burnout prevalence and the remaining 11 papers focused on interventions. Trainees reporting discrimination, abuse or harassment at least once a month were significantly more likely to experience burnout regardless of gender. Conflicting results were found on burnout prevalence and training level. Interventions identified included mindfulness courses, mentorship programmes, Enhanced Stress Resilience Training (ESRT) and Self-Compassion for Healthcare Communities (SCHC) training. Dedicated faculty and wellness opportunities produced lower burnout rates (p=0.02). Two months of mindfulness training via the Headspace application also reduced burnout scores (p=0.01). ESRT reduced overall burnout by 38.9%. Similarly, increased self-compassion significantly predicted burnout reduction (p=0.018). No significant improvement was identified in residents at unionised programmes. Conclusions: While there was no significant difference in burnout between genders, female and junior trainees are more at risk of exposure to negative behaviours in the workplace. This can directly contribute to higher levels of burnout. Interventions like mentorship and mindfulness and resilience training may reduce burnout and improve surgical trainee well-being. However, more needs to be done to educate faculty and raise awareness amongst surgical peers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".