Are Women Physicians Underrecognized for National Awards in Neuromuscular and Electrodiagnostic Medicine? An Observational Study
Bibliographic record
Abstract
INTRODUCTION/AIMS: Institutions and organizations, including the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM), have committed to embracing principles of equity, diversity, and inclusion. Notwithstanding this commitment, studies repeatedly demonstrate that women physicians are less likely to receive awards in medicine and research compared to their male counterparts. Whether women physicians are less likely to be recognized with AANEM awards is unknown. The objective of this study was to evaluate whether there is a gender disparity in the AANEM's annual awards. METHODS: In this retrospective observational study, lists of award winners were obtained from the AANEM website. Award winners' gender was assigned by three independent reviewers based on searches of public professional websites according to established methodology. Data were analyzed using descriptive statistics. RESULTS: Of 154 physician awards from 1957 to 2023, 24 (15.6%) were awarded to women and 135 (84.4%) to men. The first woman to win an AANEM award was in 2003. As the number of award categories increased over time (from 1 pre-1994 to 9 as of 2019), so too did the proportion of women winners. From 1994 to 2003, 3.4% of AANEM awardees were women compared to 17.1% from 2004 to 2013 and 18% from 2014 to 2023. Even over time, the greatest disparities existed for the Distinguished Physician/Researcher and Lifetime Achievement awards. DISCUSSION: For the AANEM, there is a notable gender gap in physician awards, but this gap has narrowed over time. Further efforts to address systemic barriers contributing to this disparity are warranted.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".