Analysis of H-index in Assessing Gender Differences in Academic Rank and Leadership in Physical Medicine and Rehabilitation in the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: The aims of the study were (1) to establish potential gender differences in academic physical medicine and rehabilitation faculty across the United States and Canada and (2) to evaluate associations between physician gender, leadership position, and research productivity. DESIGN: Physical medicine and rehabilitation programs enlisted in Fellowship and Residency Electronic Interactive Database (n = 72) and Canadian Resident Matching Service (n = 9) were searched for academic faculty with Doctor of Medicine degrees to generate a database of gender and academic profiles. Bibliometric data were collected using Elsevier's Scopus and analyzed by Strata v14.2. RESULTS: Of 1045 faculty meeting the inclusion criteria, 653 were men and 392 were women. Men were found in greater numbers across all academic ranks, with professors as most conspicuous (79.14%), and held most (85.54%) leadership positions. The study's prediction model assessed for gender differences in academic rank and leadership roles and found that odds of men having higher h-index as 0.78 (95% confidence interval = 0.24-0.87), indicating that women were not significantly inferior in academic performance. CONCLUSIONS: A significantly greater number of men make up physical medicine and rehabilitation faculty in all academic ranks and leadership positions. H-index based on gender and adjusted for covariates is comparable between men and women, suggesting that more complex, multifactorial issues are likely influencing the gender differences.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".