Toward an understanding of sexual harassment in neurosurgery
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was the creation and administration of a survey to assess the depth and breadth of sexual harassment across neurosurgery. METHODS: A survey was created to 1) assess perceived attitudes toward systemic issues that might be permissive of sexual harassment; 2) measure the reported prevalence and severity of sexual harassment; and 3) determine the populations at highest risk and those most likely to perpetrate sexual harassment. Demographic information was also included to facilitate further analysis. The SurveyMonkey platform was used, and a request to complete the survey was sent to all Society of Neurological Surgeons and Congress of Neurological Surgeons (CNS) active and resident members as well as CNS transitional, emeritus, and inactive members. Data were analyzed using RStudio version 1.2.5019. RESULTS: Nearly two-thirds of responders indicated having witnessed sexual harassment in some form (62%, n = 382). Males were overwhelmingly identified as the offenders in allegations of sexual harassment (72%), with individuals in a "superior position" identified as offenders in 86%. Less than one-third of responders addressed the incidents of sexual harassment when they happened (yes 31%, no 62%, unsure 7%). Of those who did report, most felt there was either no impact or a negative one (negative: 34%, no impact: 38%). Almost all (85%) cited barriers to taking action about sexual harassment, including retaliation/retribution (87%), impact on future career (85%), reputation concerns (72%), and associated stress (50%). Female neurosurgeons were statistically more likely than male neurosurgeons to report witnessing or experiencing sexual harassment, as well as assessing it as a problem. CONCLUSIONS: This study demonstrates that neurosurgeons report significant sexual harassment across all ages and practice settings. Sexual harassment impacts both men and women, with more than half personally subjected to this behavior and two-thirds having witnessed it. Male dominance, a hierarchical environment, and a permissive environment remain prevalent within the neurosurgical community. This is not just a historical problem, but it continues today. A change of culture will be required for neurosurgery to shed this mantle, which must include zero tolerance of this behavior, new policies, awareness of unconscious bias, and commitment to best practices to enhance diversity. Above all, it will require that all neurosurgeons and neurosurgical leaders develop an awareness of sexual harassment in the workplace and establish consistent mechanisms to mitigate against its highly deleterious effects in the specialty.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".