Letter: A Scoping Review of Burnout in Neurosurgery
Bibliographic record
Abstract
To the Editor: We read with great interest the paper by Mackel et al,1 “A Scoping Review of Burnout in Neurosurgery,” in which the authors published a comprehensive view of burnout among US neurosurgeons. Their analysis can be regarded as timely because all the studies included have been published in the last 10 yr, relevant both for the economic burden and for the compromised quality of care, and forthright given that it identifies in wellness programs emphasizing solidarity a way to counteract the problem.2 The strengths of their research lie in having analyzed studies conducted on both residents and attendings and having compared burnout rate in neurosurgery vs other US specialties. The topic is of great interest, and the possible implications involve different realities and neurosurgical contexts transversally. We believe it is important burnout in neurosurgery is not an absolute and fixed entity; on the contrary, it is strongly influenced by the broader cultural context in which it is embedded. Therefore, when looking from a global perspective, analyzing the way the phenomenon is addressed and its relevance would be of interest. This holds true for extra US realities especially. Focusing on Europe first, a difference to outline relates to the perception of burnout. The latter is much lower, as shown by the different rate of publications on the topic: Table 6 reported a total of 4 surveys on the topic conducted in Europe, of whom 2 were French. This means many countries, including Italy, have never produced literature on the topic. In contrast, North American studies are much more numerous and “ubiquitous.” Notwithstanding with this, intercontinental comparison revealed that the United States and Canada had the lowest proportion of neurosurgery trainees at risk for burnout (11.2%), whereas Europe had the highest (26.9%).3 A first, important consideration can be made: It seems a lower burnout state recognition, and, therefore, less incentive for initiatives to prevent it translates into a doubling of the risk. Further research is needed. Moving on to Asian reality, burnout is even less investigated than in Europe (3 studies). In addition, a Chinese study published by Yu et al1,3 found that academic neurosurgeons have a significantly lower rate of burnout compared to nonacademic neurosurgeons (P < .01) even if they must work a long hour; this could be explained by the high sense of personal accomplishment, and possibly by the high salaries (P < .01).4,5 Hence a second, important consideration: How much cultural diversity weighs on burnout risk? Heterogeneity could reveal itself as a potential issue in terms of populations included, scales used to measure burnout, and hypothetical ways out. Therefore, the future challenge will be to disentangle cultural and noncultural risk factors to make effective comparisons between different realities. In thanking the author for providing such an interesting food for thought, we wish future studies on burnout in neurosurgery would shade light in contexts yet insufficiently explored worldwide and inclusively analyze and compare relevant differences in the wider framework of sociocultural diversities. Funding This study did not receive any funding or financial support. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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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 | 0.025 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.028 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".