Gender Differences in Physical Activity and Health-Related Authorships Between 1950 and 2019
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to investigate gender differences in authorship in physical activity and health research. METHODS: A bibliometric study including 23,399 articles from 105 countries was conducted to estimate the participation of female researchers in physical activity publications from 1950 to 2019. The frequency of female researchers was analyzed and classified by first and last authors and the overall percentage of female authors by region and country. RESULTS: The proportion of female first authors increased from <10% in the 50s and 80s to 55% in the last decade. On the other hand, the proportion of last authors increased from 8.7% to 41.1% in the same period. Most publications with female researchers were from the United States, Canada, Australia, Brazil, the Netherlands, Spain, England, Germany, Sweden, and China. Nine of these countries had over 50% of the articles published by female first authors. However, in all 10 countries, <50% of the articles were published by female last authors. CONCLUSIONS: The proportion of female researchers increased over time. However, regional differences exist and should be addressed in gender equity policies. There is a gap in the participation of female researchers as last authors. By actively addressing the gender gap in research, the global society can harness the full potential of all talented individuals, regardless of gender, leading to more inclusive and impactful scientific advancements.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".