Editorial: Women in pediatric and adolescent sleep, volume II
Bibliographic record
Abstract
In this volume II of ‘Women in Pediatric and Adolescent Sleep’, we highlight the diverse work of women researchers working in the area of pediatric and adolescent sleep. The seven articles in this volume are from a research area that was inspired, started or sparked by women, studies led by women, or that celebrate outstanding female researchers and their contributions to pediatric and adolescent sleep. Women likely make up the minority of sleep researchers though this may be improving. In a 2021 study from two national US sleep societies, the American Academy of Sleep Medicine and the Sleep Research Society (AASM/SRS), women made up only 17% of 164 US-based AASM/SRS award recipients over a 40-year period.1 Analysis of the trend over time did show an increase from 0% of award recipients being women in the 1980’s to 32% of recipients being women in the 2010s. US-based women physicians received proportionally fewer AASM/SRS awards than women nonphysician PhDs (10% vs 29%). A 2024 study, using data from the AASM Diversity, Equity and Inclusion annual reports, showed that women’s membership in the AASM increased from 33% in 2019 to 39% in 2022.2 Women’s representation on the AASM Foundation’s board of directors also increased from 20% for 1998 to 2017 to 50% for 2017 to 2022. Over its 50-year history, only 18% of all AASM presidents were women; the most recent 10-years, however, does show an upward trend with women comprising 40% of AASM presidents and 80% of AASM Foundations presidents in the last 5-year period (2018-2022). While these studies do not look specifically look at the representation of women researchers in sleep, these data suggest that there has been progress in the number of leadership positions held by women in sleep medicine professional organizations.Despite the improving trends of women's representation in membership, leadership, and awards, compensation inequities between women and men continue. The 2024 AASM study showed that the salaries of women physicians are 19% (approximately $62,000) less than their male counterparts. 2 Differences are still seen when calculating compensation per work relative value unit (12% lower for women) or for total benefits (15% lower for women). These gender related wage gaps are not unique to sleep physicians nor US-based physicians or researchers. Results from the analysis of publicly available Canadian data show that women physician made up less than 35% of 10 specialties with highest gross and net income. 3 By contrast, women accounted for 47%, 48%, and 62% of the specialties with the lowest income (family medicine, psychiatry, and pediatrics respectively). Differences in hours of work are not sufficient to explain this gap. A national survey of the gender pay gap of the National Health Services (NHS; publicly funded health services) in the United Kingdom was 22%, a gap that is three times larger than for private sector physicians or other public sector health professionals. 4 Discrepancies in the pay gap between males and females may be attributable, at least in part, to differences in starting salaries 5 and supplemental income 6 which are not explained by specialty, academic rank, work hours, research time and other factors. 7 Over the course of a career, this differences in wages add up with one study estimating that, on average, female US physicians earn $2 million less than male physicians across a 40-year career. 8 An analysis of data from the Surveys of Doctorate Recipients conducted by the National Science Foundation shows pay gaps between men and women science and engineering doctorate holders working in both academia and industry. 9 For the time period 1995-2017, women earned 5.3% less than men in academia, compared to 3.5% less in industry. This gap was similar for the 1995-2003 and 2006-2017 periods. The pay gap evolves differently across sectors with equal pay for men and women to start in academia but a widening salary gap over career age. In contrast, men earn more to start with in industry with a narrowing of the gap across career age. Reducing and removing these inexplicable inequities in salary and compensation is important to attracting women physicians and researchers to pediatric and adolescent sleep as well as to research careers more broadly.The topics covered in this research topic are diverse showing the breadth and depth of research in pediatric and adolescent sleep. This includes topics spanning a range in age (young children to adolescents), employing a variety of methodological approaches (e.g., mixed methods), focusing on both existing and emerging technologies (e.g., non-invasive ventilation and machine learning) and addressing several pediatric sleep disorders including insomnia and sleep disordered breathing. A study from Sturiudottir and colleagues shows the usefulness of machine learning in sleep analysis (doi.org/10.3389/frsle.2023.1082996). Dobson and colleagues explore the impact of the covid-19 pandemic on children using non-invasive ventilation using thematic analysis (doi.org/10.3389/frsle.2023.1328558). A mixed-methods analysis of bedtime routine by Papadopoulou and colleagues highlights both variance in bedtime routines and common barriers to improving bedtime routines (doi.org/10.3389/frsle.2024.1339561). A brief report from Bauducco and colleagues show that popularity in adolescent girls comes at a cost of more insomnia symptoms (doi.org/10.3389/frsle.2024.1346806). A systematic review of the relationship between sleep and physical activity by Wang and colleagues shows that variation in the measurements of sleep parameters and non-standardization of exercise protocols limits the ability to combine data for meta-analysis (doi.org/10.3389/fneur.2024.1438786). Lacki and colleagues used polysomnography data to explore the characteristic features of children diagnosed with attentiondeficit hyperactivity disorder ± obstructive sleep apnea (doi.org/10.3389/frsle.2024.1451869). Finally, Olmstead and colleagues used framework analysis to explore the experience of children using long-term non-invasive ventilation (doi.org/10.3389/frsle.2024.1459349).For additional research from women in sleep, please see these Frontiers Research topics:https://www.frontiersin.org/research-topics/59552/women-in-insomniahttps://www.frontiersin.org/research-topics/59319/women-in-neurology-sleep-disordershttps://www.frontiersin.org/research-topics/55217/women-in-sleep-and-breathinghttps://www.frontiersin.org/research-topics/47458/women-in-pediatric-and-adolescent-sleep/magazine
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".