Sex, Gender, and Equity in Cardiovascular Medicine, Surgery, and Science in Canada: Challenges, Successes, and Opportunities for Change
Bibliographic record
Abstract
BACKGROUND: A previous review of sex, gender, and equity within cardiovascular (CV) medicine, surgery, and science in Canada has revealed parity during medical and graduate school training. The purpose of this study was to explore sex and gendered experiences within the Canadian CV landscape, and their impact on career training and progression. METHODS: An environmental scan was conducted of the Canadian CV landscape, which included an equity survey using Qualtrics software. RESULTS: The environmental scan revealed that women remain underrepresented within CV training programs as trainees (12%-30%), program directors (33%), in leadership roles at the divisional level (21%), and in other professional or career-related activities (< 30%). Our analysis also showed improvements of career engagement at these levels of women at over time. The thematic analysis of the equity survey responses (n = 71 respondents; 83% female; 9.7% response rate among female Canadian Cardiovascular Society members) identified the following themes reported within the socio-ecological framework: desire to report inequities vs staying the course (individual level); desire for social support and mentorship and challenges of dual responsibilities (interpersonal level); concerns over exclusionary cliques and desire for respect and opportunity (organizational level); and increasing awareness and actions to overcome institutional barriers and accountability (societal level). CONCLUSIONS: Although women face challenges and remain underrepresented in CV medicine, surgery, and science, this study highlights potential opportunities for improving access of female medical, surgical, and research trainees and professionals to specialized cardiovascular training, career advancement, leadership, and research.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".