Author Interviews, Brigham & Women's - Harvard, Education, Gender Differences, JAMA, Surgical Research / 09.08.2018
Why Do So Few Women Enter or Complete Surgical Residency?
MedicalResearch.com Interview with:
[caption id="attachment_43788" align="alignleft" width="200"] Dr. Rangel[/caption]
Erika L. Rangel, MD,MS
Instructor, Harvard Medical School
Trauma, Burn and Surgical Critical Care
Department of Surgery, Center for Surgery and Public Health
Brigham and Women’s Hospital
Harvard T. H. Chan School of Public Health
Boston, Massachusetts
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Although women make up half of medical student graduates in 2018, they only comprise a third of applicants to general surgery. Studies suggest that lifestyle concerns and perceptions of conflict between career and family obligations dissuade students from the field.
After entering surgical residencies, women residents have higher rates of attrition (25% vs 15%) and cite uncontrollable lifestyle as a predominant factor in leaving the field. Surgeons face reproductive challenges including stigma against pregnancy during training, higher rates of infertility, need for assisted reproduction, and increased rates of pregnancy complications. However, until recently, studies capturing the viewpoints of women who begin families during training have been limited. Single-institution experiences have described mixed experiences surrounding maternity leave duration, call responsibilities, attitudes of coworkers and faculty, and the presence of postpartum support.
Earlier this year, our group presented findings of the first national study of perspectives of surgical residents who had undergone pregnancy during training. A 2017 survey was distributed to women surgical residents and surgeons through the Association of Program Directors in Surgery, the Association of Women Surgeons and through social media via twitter and Facebook. Responses were solicited from those who had at least one pregnancy during their surgical training.
39% of respondents had seriously considered leaving surgical residency, and 30% reported they would discourage a female medical student from a surgical career, specifically because of the difficulties of balancing pregnancy and motherhood with training (JAMA Surg 2018; July 1; 153(7):644-652).
These findings suggested the challenges surrounding pregnancy and childrearing during training may have a significant impact on the decision to pursue or maintain a career in surgery. The current study provides an in-depth analysis of cultural and structural factors within residency programs that influence professional dissatisfaction.
We found that women who faced stigma related to their pregnancies, who had no formal maternity leave at their programs, and who altered subspecialty training plans due to perceived challenges balancing motherhood with the originally chosen subspecialty were most likely to be unhappy with their career or residency.
Dr. Rangel[/caption]
Erika L. Rangel, MD,MS
Instructor, Harvard Medical School
Trauma, Burn and Surgical Critical Care
Department of Surgery, Center for Surgery and Public Health
Brigham and Women’s Hospital
Harvard T. H. Chan School of Public Health
Boston, Massachusetts
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Although women make up half of medical student graduates in 2018, they only comprise a third of applicants to general surgery. Studies suggest that lifestyle concerns and perceptions of conflict between career and family obligations dissuade students from the field.
After entering surgical residencies, women residents have higher rates of attrition (25% vs 15%) and cite uncontrollable lifestyle as a predominant factor in leaving the field. Surgeons face reproductive challenges including stigma against pregnancy during training, higher rates of infertility, need for assisted reproduction, and increased rates of pregnancy complications. However, until recently, studies capturing the viewpoints of women who begin families during training have been limited. Single-institution experiences have described mixed experiences surrounding maternity leave duration, call responsibilities, attitudes of coworkers and faculty, and the presence of postpartum support.
Earlier this year, our group presented findings of the first national study of perspectives of surgical residents who had undergone pregnancy during training. A 2017 survey was distributed to women surgical residents and surgeons through the Association of Program Directors in Surgery, the Association of Women Surgeons and through social media via twitter and Facebook. Responses were solicited from those who had at least one pregnancy during their surgical training.
39% of respondents had seriously considered leaving surgical residency, and 30% reported they would discourage a female medical student from a surgical career, specifically because of the difficulties of balancing pregnancy and motherhood with training (JAMA Surg 2018; July 1; 153(7):644-652).
These findings suggested the challenges surrounding pregnancy and childrearing during training may have a significant impact on the decision to pursue or maintain a career in surgery. The current study provides an in-depth analysis of cultural and structural factors within residency programs that influence professional dissatisfaction.
We found that women who faced stigma related to their pregnancies, who had no formal maternity leave at their programs, and who altered subspecialty training plans due to perceived challenges balancing motherhood with the originally chosen subspecialty were most likely to be unhappy with their career or residency.
![MedicalResearch.com Interview with: Dr. Theodore Satterthwaite MD Assistant professor in the department of Psychiatry, and Cedric Xia, a MD-PhD candidate Perelman School of Medicine at the University of Pennsylvania MedicalResearch.com: What is the background for this study? What are the main findings? Response: Unlike other branches of modern medicine, psychiatry still solely replies on patient reports and physician observations for clinical decision-making. Without biologically-based tests, the diagnostic categories for mental health do not carve nature at its joint. This is evident in the high levels of co-morbidity across disorders and heterogeneity within disorders. Through this research, we studied a large sample of adolescents who completed MRI-based functional imaging, and used recently-developed machine learning techniques to uncover specific abnormalities that are highly predictive of a wide variety of psychiatric symptoms. Essentially, we tried to find brain patterns that were predictive of different types of psychiatric symptoms. We discovered four such brain-guided dimensions of psychopathology: mood, psychosis, fear, and disruptive behavior. While each of these dimensions exhibits a unique pattern of brain connectivity, a common feature of brain anomaly is shared across the dimensions. Notably, in all linked dimensions, the default mode network and fronto-parietal network, two brain regions that usually become increasingly distinct as the brain matures, were abnormally connected. This loss of normal brain network segregation supports the hypothesis that many psychiatric illnesses may be disorders of brain development. MedicalResearch.com: What should readers take away from your report? Response: This study shows that we can start to use the brain to guide our understanding of psychiatric disorders in a way that’s fundamentally different than grouping symptoms into clinical diagnostic categories. By moving away from clinical labels developed decades ago, we can begin to let the biology speak for itself. Our ultimate hope is that understanding the biology of mental illnesses will allow us to develop better treatments for our patients. MedicalResearch.com: What recommendations do you have for future research as a result of this work? Response: This study demonstrates the importance of incorporating vast amounts of biological data to study mental illness across clinical diagnostic boundaries. Moving forward, we hope to integrate genomic data in order to describe pathways from genes to brain to symptoms, which could ultimately be the basis for novel treatments for mental illness. MedicalResearch.com: Is there anything else you would like to add? Response: Future breakthroughs in brain science to understand mental illness requires large amount of data. While the current study takes advantage of one of the largest samples of youth, the size (n=999) remains dwarfed by the complexity of the brain. The neuroscience community is actively working towards collecting higher quality data in even larger samples, so we can validate and build upon the findings. Citation: Cedric Huchuan Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel García de la Garza, Simon N. Vandekar, Zaixu Cui, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett, Theodore D. Satterthwaite. Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-05317-y [wysija_form id="3"] [last-modified] The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition. Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.](https://medicalresearch.com/wp-content/uploads/Cross-clinical-diagnostic-categories.jpg)
![MedicalResearch.com Interview with: Dr. Theodore Satterthwaite MD Assistant professor in the department of Psychiatry, and Cedric Xia, a MD-PhD candidate Perelman School of Medicine at the University of Pennsylvania MedicalResearch.com: What is the background for this study? What are the main findings? Response: Unlike other branches of modern medicine, psychiatry still solely replies on patient reports and physician observations for clinical decision-making. Without biologically-based tests, the diagnostic categories for mental health do not carve nature at its joint. This is evident in the high levels of co-morbidity across disorders and heterogeneity within disorders. Through this research, we studied a large sample of adolescents who completed MRI-based functional imaging, and used recently-developed machine learning techniques to uncover specific abnormalities that are highly predictive of a wide variety of psychiatric symptoms. Essentially, we tried to find brain patterns that were predictive of different types of psychiatric symptoms. We discovered four such brain-guided dimensions of psychopathology: mood, psychosis, fear, and disruptive behavior. While each of these dimensions exhibits a unique pattern of brain connectivity, a common feature of brain anomaly is shared across the dimensions. Notably, in all linked dimensions, the default mode network and fronto-parietal network, two brain regions that usually become increasingly distinct as the brain matures, were abnormally connected. This loss of normal brain network segregation supports the hypothesis that many psychiatric illnesses may be disorders of brain development. MedicalResearch.com: What should readers take away from your report? Response: This study shows that we can start to use the brain to guide our understanding of psychiatric disorders in a way that’s fundamentally different than grouping symptoms into clinical diagnostic categories. By moving away from clinical labels developed decades ago, we can begin to let the biology speak for itself. Our ultimate hope is that understanding the biology of mental illnesses will allow us to develop better treatments for our patients. MedicalResearch.com: What recommendations do you have for future research as a result of this work? Response: This study demonstrates the importance of incorporating vast amounts of biological data to study mental illness across clinical diagnostic boundaries. Moving forward, we hope to integrate genomic data in order to describe pathways from genes to brain to symptoms, which could ultimately be the basis for novel treatments for mental illness. MedicalResearch.com: Is there anything else you would like to add? Response: Future breakthroughs in brain science to understand mental illness requires large amount of data. While the current study takes advantage of one of the largest samples of youth, the size (n=999) remains dwarfed by the complexity of the brain. The neuroscience community is actively working towards collecting higher quality data in even larger samples, so we can validate and build upon the findings. Citation: Cedric Huchuan Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel García de la Garza, Simon N. Vandekar, Zaixu Cui, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett, Theodore D. Satterthwaite. Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-05317-y [wysija_form id="3"] [last-modified] The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition. Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.](https://medicalresearch.com/wp-content/uploads/Cross-clinical-diagnostic-categories-200x180.jpg)
