Women Empowering Women: Assessing the American College of Surgeons Women in Surgery Committee Mentorship Program
Bibliographic record
Abstract
BACKGROUND: Mentorship is an important factor for career promotion and professional development. The Women in Surgery Committee developed a mentorship program that matched early career female surgeons to senior female surgeons for 1 year. We hypothesized participation in the program would empower junior surgeons by providing opportunities to network and hone skills necessary to attain their career goals. METHODS: Survey was sent 4 to 6 weeks after program completion. Statements about mentorship and value of the Women in Surgery Committee program were ranked on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Participants were compared based on frequency of encounters using Student's t-test. RESULTS: A total of 105 pairs were identified; response rate was 60%. Results reported as (mean ± SD). Participants believed mentorship was essential for young surgeons (4.5 ± 1.0), and limiting the program to female surgeons added value (4.4 ± 0.6). When compared with mentees who met less than 4 times in a year, those who met 4 or more times perceived the program as beneficial (4.4 ± 0.82, p < 0.001). Mentees who met 4 or more times in a year benefitted from creating and achieving goals (4.3 ± 0.75, p < 0.001), setting expectations (4.5 ± 0.6, p < 0.001), providing networking opportunities (4.1 ± 1.1, p < 0.05), and developing professional skills (3.9 ± 0.98). CONCLUSION: The Women in Surgery Committee Mentorship Program provides an opportunity for young female surgeons; however, perceived benefit is dependent on mentee engagement.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".