Gender Diversity, Leadership, Promotion, and Opportunity Among the Members of the Orthopaedic Trauma Association (OTA)
Bibliographic record
Abstract
OBJECTIVES: To demonstrate the gender distribution in leadership positions and academic promotion of Orthopaedic Trauma Association (OTA) members. METHODS: We conducted a cross-sectional examination of the 2020-2021 OTA membership cohort dataset provided by the OTA. Professional and academic information of OTA members at their site of appointment was also abstracted from publicly available online resources. Data included: gender, OTA membership category, OTA leadership position, trauma fellowship completion, trauma practice setting, level of trauma center, percentage of trauma work, year of first practice, academic rank, and university/hospital/institutional leadership role. Statistical analysis included chi-squared, Wilcoxon two-sample, and Fisher exact tests. RESULTS: 2608 OTA members were identified; 14.1% were women. Female representation was highest in the Trauma Practice Professional category (67.1%) and significantly lower in the Active category (9.1%) ( P < 0.0001). No statistically significant gender differences were observed regarding level of trauma center, percentage of trauma work, or trauma practice setting. In the Active, Clinical and Emeritus categories, men achieved a higher level of academic rank than women at their site of employment ( P = 0.003), while more men completed trauma fellowships ( P = 0.004) and had been in practice for significantly longer ( P < 0.0001). Men held more of the highest leadership positions (eg, Board of Directors) ( P = 0.0047) and the greatest number of leadership positions ( P = 0.017) within the OTA compared with women. CONCLUSION: Gender disparity exists within the upper echelon of leadership and academic representation in orthopaedic trauma. Our findings will help inform strategic policies to address gender diversity within the OTA and the broader orthopaedic trauma subspecialty.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".