Gender Representation Among Major Plastic Surgery Society Achievement Award Recipients
Bibliographic record
Abstract
Purpose: Professional achievement awards are an important factor in recruitment, promotion, and faculty review within academic institutions. Studies have shown that subconscious, gender-based assumptions of individuals and their work in traditionally male-dominated fields lead to more positive evaluations of men than women, a phenomenon present among scientific and medical award committees. This study examined gender representation among recipients of major North American plastic surgery society awards over the last 50 years. Methods: Recipient lists of major achievement awards bestowed by ten American and Canadian plastic surgery societies between 1970 and 2020 were accessed online or by direct contact with the society. Awardee gender, institution affiliation, graduation year, fellowship status, and additional major awards received were recorded. Comparisons were made between gender representation among society presidents, board membership, general society memberships, attending physicians, and plastic surgery residency enrolment. Results: Thirty-two major awards given by ten plastic surgery societies were included. Six hundred and twenty-five awards were conferred, of which 47 recipients were female (7.5%). Of the 121 individuals that received multiple major awards, 8 were female. Two-thirds of female awardees (72%) were clinical plastic surgeons and the remainder were scientists. Over the past 50 years, there has been a gradual increase in the proportion of female award winners. Conclusions: Despite a gradual increase in the proportion of female awardees in major plastic surgery societies, female plastic surgeons remain underrepresented among awardees, with less than 10% of major awards conferred to females.
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 | 0.003 | 0.007 |
| 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) | 0.006 | 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".