Examining gender diversity growth as a model for inclusion of all underrepresented persons in medical physics
Bibliographic record
Abstract
The labor force of Medical Physics is one of the most gender diverse in the field of Physics, as it has attained the proportional achievement of ~30% women worldwide (Tsapaki et al. Phys Medica. 2018;55:33-39). While great strides have been made toward a gender diverse workforce, women still comprise an underrepresented group. Many strategies have been suggested to increase the participation of underrepresented persons by addressing unconscious biases, increasing opportunities, dedicated hiring policies, and providing support networks in science and medicine (Barabino et al. Sci Eng Ethics. 2019; Coe et al. Lancet. 2019), yet the personnel landscape remains largely uniform. Herein, the conditions, strategies, and approaches that facilitated gender diversity in Medical Physics are considered as a means to further the inclusion of other underrepresented groups through exemplars of mentorship, addressing unconscious biases and the implementation of inclusive practices. Furthermore, the potential for gender diversity to act as a catalyst to create an environment that is more accepting of diversity and supports and encourages inclusive practices for the participation and inclusion of other underrepresented groups in Medical Physics is discussed.
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".