The queen bee phenomenon in Canadian surgical subspecialties: An evaluation of gender biases in the resident training environment
Bibliographic record
Abstract
BACKGROUND: The queen bee phenomenon (QBP) describes the behavioural response that occurs when women achieve success in a male-dominated environment, and in this position of authority, treat their female subordinates more critically. It has been demonstrated in business, academia, the military, and police force. The goal of this study was to determine whether the QBP occurs in surgical specialties. We hypothesized that female surgeons, fellows, and senior surgical residents would be more critical in their assessment of junior female residents than their male counterparts. METHODS: A scenario-based survey was distributed via email to all Canadian surgical programs between February and March 2021. Scenarios were designed to assess either female or male learners. Centers distributed surveys to attending surgeons, surgical fellows, resident physicians, and affiliate surgeons. Respondents average Likert score for female-based and male-based questions were calculated. Subgroup analyses were performed based on gender, age, seniority, and surgical specialty. RESULTS: 716 survey responses were collected, with 387 respondents identifying as male (54%) and 321 identifying as female (45%). 385 attending surgeons (54%), 66 fellows (9%), and 263 residents (37%) responded. The mean Likert scores for female respondents assessing female learners was significantly lower than male learners (p = 0·008, CI = 95%). During subgroup analysis, some specialties demonstrated significant scoring differences. DISCUSSION: The QBP was shown to be present among surgical specialties. Female respondents assessed female learners more critically than their male counterparts. CONCLUSION: These findings highlight the importance of tackling organizational biases to create more equitable educational and work environment in surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".