Presenters at chiropractic research conferences 2010–2019: is there a gender equity problem?
Bibliographic record
Abstract
BACKGROUND: Presenting at professional and scientific conferences can be an important part of an individual's career advancement, especially for researchers communicating scientific findings, and can signal expertise and leadership. Generally, women presenting at conferences are underrepresented in various science disciplines. We aimed to evaluate the gender of presenters at research-oriented chiropractic conferences from 2010 to 2019. METHODS: We investigated the gender of presenters at conferences hosted by chiropractic organisations from 2010 to 2019 that utilised an abstract submission process. Gender classification was performed by two independent reviewers. The gender distribution of presenters over the ten-year period was analysed with linear regression. The association of conference factors with the gender distribution of presenters was also assessed with linear regression, including the gender of organising committees and abstract peer reviewers, and the geographic region where the conference was hosted. RESULTS: From 39 conferences, we identified 4,340 unique presentations. Women gave 1,528 (35%) of the presentations. No presenters were classified as gender diverse. Overall, the proportion of women presenters was 30% in 2010 and 42% in 2019, with linear regression demonstrating a 1% increase in women presenting per year (95% CI = 0.4-1.6%). Invited/keynote speakers had the lowest proportion of women (21%) and the most stagnant trajectory over time. The gender of conference organisers and abstract peer reviewers were not significantly associated with the gender of presenters. Oceanic conferences had a lower proportion of women presenting compared to North America (27% vs. 36%). CONCLUSIONS: Overall, women gave approximately one-third of presentations at the included conferences, which gradually increased from 2010 to 2019. However, the disparity widens for the most prestigious class of keynote/invited presenters. We make several recommendations to support the goal of gender equity, including monitoring and reporting on gender diversity at future conferences.
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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 | 0.033 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".