Mind the gap: Gender disparities in authorship in the Journal of Medical Imaging and Radiation Sciences
Bibliographic record
Abstract
INTRODUCTION: Research studies tracking gender and academic publication productivity in healthcare find gender disparities in research activity, publication, and authorship. Article authorship is one of the important metrics to track when seeking to understand gender inequality in academic career advancement. Research on gender disparities in publication productivity in the field of Medical Radiation Science (MRS) is very limited thus this study analyses and explains potential gender differences in article authorship and acceptance for publication in the Journal of Medical Imaging and Radiation Sciences (JMIRS) for a 5-year period (2017-2021). METHODS: Gender was inferred based on the author's first name or title (e.g., Mr, Mrs or Ms). For those who left the title blank or reported as 'Dr' or 'Prof,' a series of steps were taken to identify their gender. Where gender was impossible to ascribe, these authors were excluded. Descriptive and inferential statistics are reported for the study population. Descriptive and inferential statistics are used. Percentages of females are reported, and males constitute the other portion. Chi-square, slope analysis and z-tests were used to test hypotheses. RESULTS: Results show that female authorship overall and in all categories of authorship placement (i.e., first, last and corresponding) increased over the timeframe reviewed. The percentage gain in the increase was higher than that for male authorship. However, male authorship started from a higher baseline in 2017 and has also increased year on year and overall, as well as in each placement category examined. More female authors were in the MRS sub-specialism Radiation Therapy (RT) than in the other MRS sub-specialisms. Analysis of the acceptance rate of articles with female authors shows a weak downward trend, and this may be related to higher submission and acceptance rates of articles by male authors during the same period. CONCLUSION: Male authors are overrepresented in all categories, which raises questions about the persistence of gender disparities in JMIRS authorship and article acceptance. Positive trends in female authorship indicate progress, yet there is the persistence of the significant under-representation of women in the Medical Radiation Sciences workforce in academic publishing. Recruiting more males to address the gender imbalance in the profession should not be at the expense of females' career progression.
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".