Moral Distress and Moral Injury Among Attending Neurosurgeons: A National Survey
Bibliographic record
Abstract
BACKGROUND: "Moral distress" describes the psychological strain a provider faces when unable to uphold professional values because of external constraints. Recurrent or intense moral distress risks moral injury, burnout, and physician attrition but has not been systematically studied among neurosurgeons. OBJECTIVE: To develop a unique instrument to test moral distress among neurosurgeons, evaluate the frequency and intensity of scenarios that may elicit moral distress and injury, and determine their impact on neurosurgical burnout and turnover. METHODS: An online survey investigating moral distress, burnout, and practice patterns was emailed to attending neurosurgeon members of the Congress of Neurological Surgeons. Moral distress was evaluated through a novel survey designed for neurosurgical practice. RESULTS: A total of 173 neurosurgeons completed the survey. Half of neurosurgeons (47.7%) reported significant moral distress within the past year. The most common cause was managing critical patients lacking a clear treatment plan; the most intense distress was pressure from patient families to perform futile surgery. Multivariable analysis identified burnout and performing ≥2 futile surgeries per year as predictors of distress (P < .001). Moral distress led 9.8% of neurosurgeons to leave a position and 26.6% to contemplate leaving. The novel moral distress survey demonstrated excellent internal consistency (Cronbach alpha = 0.89). CONCLUSION: We developed a reliable survey assessing neurosurgical moral distress. Nearly, half of neurosurgeons suffered moral distress within the past year, most intensely from external pressure to perform futile surgery. Moral distress correlated with burnout risk caused 10% of neurosurgeons to leave a position and a quarter to consider leaving.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".