Female authorship positions in health economic evaluations: a cross-sectional analysis
Bibliographic record
Abstract
To investigate the gender of the authors who publish articles of health economic evaluations in medicine and healthcare journals. We evaluated a random sample of economic evaluations indexed in MEDLINE during 2019. Gender of the first, last and corresponding author was determined by review of the author's first name. Data were summarized as frequency and percentage for categorical items and median and interquartile range (IQR) for continuous items. We also calculated the index of authors per paper. We included 200 studies with 1365 authors (median of 6 authors per paper; IQR: 4-9). Gender identification was possible for all authors in the study sample: 802 (59%) were men and 563 (41%) were women. The number of female first, last, and corresponding authors respectively were 78 (39%), 68 (34%), and 80 (40%) for health economic evaluations. Female scientists were underrepresented as co-authors and in prominent authorship positions in health economic evaluations. This study serves as a call to action for the scientific community to actively work towards equity and inclusion. Investigar el género de las personas autoras que publican artículos de evaluaciones económicas de la salud en revistas de medicina y atención sanitaria. Se evaluó una muestra aleatoria de evaluaciones económicas indexadas en MEDLINE durante 2019. El género de las personas autoras como primera, última y autoría de correspondencia se determinó mediante la revisión del nombre de la persona firmante. Los datos se resumieron como frecuencia y porcentaje para las variables categóricas, y como mediana y rango intercuartílico (RIC) para las variables continuas. También se calculó el índice de autores por artículo. Se incluyeron 200 estudios con 1365 autores (mediana de 6 autores por artículo; RIC: 4-9). La identificación del sexo fue posible para toda la muestra: 802 (59%) eran hombres y 563 (41%) eran mujeres. Los números de científicas con una posición como primera, última y autora de correspondencia fueron 78 (39%), 68 (34%) y 80 (40%), respectivamente, para las evaluaciones económicas. Las científicas estuvieron infrarrepresentadas como coautoras y en puestos de autoría destacados en las evaluaciones económicas. Este estudio sirve como una llamada a la acción para que la comunidad científica trabaje activamente por la equidad y la inclusión.
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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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".