Gender and Generational Influences on the Pediatric Workforce and Practice
Bibliographic record
Abstract
In response to demographic and other trends that may affect the future of the field of pediatrics, the Federation of Pediatric Organizations formed 4 working groups to participate in a year's worth of research and discussion preliminary to a Visioning Summit focusing on pediatric practice, research, and training over the next 2 decades. This article, prepared by members of the Gender and Generations Working Group, summarizes findings relevant to the 2 broad categories of demographic trends represented in the name of the group and explores the interface of these trends with advances in technology and social media and the impact this is likely to have on the field of pediatrics. Available data suggest that the trends in the proportions of men and women entering pediatrics are similar to those over the past few decades and that changes in the overall ratio of men and women will not substantially affect pediatric practice. However, although women may be as likely to succeed in academic medicine and research, fewer women than men enter research, thereby potentially decreasing the number of pediatric researchers as the proportion of women increases. Complex generational differences affect both the workforce and interactions in the workplace. Differences between the 4 generational groups comprising the pediatric workforce are likely to result in an evolution of the role of the pediatrician, particularly as it relates to aspects of work-life balance and the use of technology and social media.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".