Beyond Persistence: Increasing the Representation of Women Faculty and Leaders in Academic Surgery
Bibliographic record
Abstract
In demanding tripartite roles, faculty at Academic Health Sciences Centres provide surgeon training and patient care, while seeking discovery through research and innovation. The persistent imbalance of women in academic surgery has been empirically evident and an intense topic of discussion for decades, yet solutions remain elusive. There has been increasing analysis and scrutiny of the factors affecting women in this domain, while highlighting the disconnect between the current state and our affirmed belief in gender equity in both education and medicine. My Organizational Improvement Plan is focussed on the recognition and resolution of barriers and biases impeding the appointment and promotion of women into faculty and leadership positions in the Department of Surgery at an Ontario University. It will explore the literature; outline theoretical underpinnings (critical theory, feminist theory, social cognition theory); and provide insight into the realm of implicit bias. It will engage authentic and transformative leadership and propose the use of appreciative inquiry as a change implementation framework for an integrated solution. This scholarly work aligns with an overriding public sentiment advocating for change of a social justice nature. Although my doctoral work is limited in scope to women in academic surgery for manageability reasons, it has the potential for scaling and broader application to address inequities that continue to exist for all equity-deserving groups. This is more than the right thing to do. We have a responsibility and obligation in health care and education to pursue equity and social justice.
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".