Diversity among endovascular neurointerventionalists in Canada results of a national survey 2022
Bibliographic record
Abstract
AimNeurointervention (NIR) is a relatively new developing filed of medicine. Diversity and inclusion in various medical fields has made a significant progress. However, many surgical and interventional fields are still lagging in this respect. The aim of this study was to evaluate the degree of diversity and inclusion amongst neurointerventionalists in Canada.Materials and methodsA survey was completed in June 2022 by each neurointerventional division in Canada. The survey included questions regarding demographics, inclusivity, diversity, social and personal parameters. The collected data was analysed using semi-quantitative analysis.ResultsAs of 2022, 85 physicians were actively practicing NIR in Canada. 52% were neuroradiologists, 38% neurosurgeons and 9% neurologists. 41% were immigrants to Canada (from 19 countries), for 35% English or French were not first language, 35% were visible minority. Women comprised only 21% of the practitioners, with comparable proportion of women in leadership positions. Most practitioners were in the 30-49 age group. 2.4% practitioners identified as LGBTQ. There was no gender difference in terms of life to work balance, with majority of practitioners being engaged in long term relationships and having children.ConclusionsOur study shows encouraging results in terms of diversity and inclusion amongst Canadian neurointerventionalists regarding the representation of various specialty backgrounds, immigrants, and visible minorities. NIR centers are distributed according to population density and better coverage is needed in smaller communities and remote/isolated areas. Both women and men Canadian neurointerventionalists seem to have a favourable life-work balance. Gaps still exist regarding inclusion of first nations and women which are under-represented among Canadian Neurointerventionalists. Women however are proportionally serving in leadership positions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".