Dissecting disparity: improvements towards gender parity in leadership and on the podium within the Canadian Orthopaedic Association
Bibliographic record
Abstract
Objectives The purpose of this paper was to analyse the 15-year trend of women in leadership roles within the Canadian Orthopaedic Association (COA). This included not only leadership positions at the committee level in the association but also the more visible podium positions in the annual meeting programme: research podium and poster presentations, session moderators, panellists and faculty. Methods Data on the numbers of male and female members were gathered from COA membership records for the most recent 5 years (2014–2018), as well as for 10 years previous (2009) and 15 years previous (2004). Male and female representation on COA committees, as well as the number of presenters at the annual meeting was calculated. Descriptive data were generated to compare the changes in gender representation over time. Results In Canada, in 2018, 11.2% of orthopaedic surgeons were female. Within the COA, 17.6% of the members are female, with active female surgeons comprising 11.6% of the total membership. The largest increase in representation of women within the COA is in the trainee category which is 25.3% female. At the 2018 annual meeting, 25% of the attendees were women, with 22% of all podium appearances by women. Not including research presentations, women participated as faculty in 11% of the appearances at the 2018 annual meeting. Conclusion In conclusion, gender parity is not yet a reality in Canadian orthopaedics; however, the number of females in leadership roles and on the podium is consistent with the current gender diversity within the COA membership. Further efforts will be required to improve gender diversity as well as to encourage female medical students to consider orthopaedics as a specialty. The availability of female role models that are visible on the podium and in leadership positions may be one strategy to encourage the journey toward gender parity.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".