Author Interviews, Brigham & Women's - Harvard, Education, JAMA / 06.05.2019
What Makes Academic Physicians Satisfied With Their Job?
MedicalResearch.com Interview with:
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Dr. Simpkin[/caption]
Arabella L. Simpkin, MD, MMSc
Associate Director, Center for Educational Innovation and Scholarship, MGH
Associate Program Director, Education and Curriculum, Internal Medicine Residency, MGH
Instructor in Medicine, Harvard Medical School
Boston, MA 02114
MedicalResearch.com: What is the background for this study?
Response: The academic health care environment has changed in unprecedented ways over several decades, with mounting evidence that faculty are becoming increasingly more unhappy, dissatisfied, and burnt out in their work. Concern for faculty wellbeing is high, with much speculation about causes of burnout. Comprehending what affects satisfaction at work in academic health care centers is critically important to stem this epidemic of discontent. For physicians, satisfaction has been reported to be associated with quality of care delivered, particularly as measured by patient satisfaction; faculty retention and job satisfaction are intricately linked, with dissatisfied physicians more likely to leave the profession and to discourage others from entering.
Other industries that have suffered similar rises in employee discontent have found that demonstration of respect is the most important leadership behavior in improving employees satisfaction. To our knowledge this factor has not been looked at in healthcare professionals. To address this gap, we sought to determine key variables influencing satisfaction at work for faculty in a large academic medical center in the United States.
Dr. Simpkin[/caption]
Arabella L. Simpkin, MD, MMSc
Associate Director, Center for Educational Innovation and Scholarship, MGH
Associate Program Director, Education and Curriculum, Internal Medicine Residency, MGH
Instructor in Medicine, Harvard Medical School
Boston, MA 02114
MedicalResearch.com: What is the background for this study?
Response: The academic health care environment has changed in unprecedented ways over several decades, with mounting evidence that faculty are becoming increasingly more unhappy, dissatisfied, and burnt out in their work. Concern for faculty wellbeing is high, with much speculation about causes of burnout. Comprehending what affects satisfaction at work in academic health care centers is critically important to stem this epidemic of discontent. For physicians, satisfaction has been reported to be associated with quality of care delivered, particularly as measured by patient satisfaction; faculty retention and job satisfaction are intricately linked, with dissatisfied physicians more likely to leave the profession and to discourage others from entering.
Other industries that have suffered similar rises in employee discontent have found that demonstration of respect is the most important leadership behavior in improving employees satisfaction. To our knowledge this factor has not been looked at in healthcare professionals. To address this gap, we sought to determine key variables influencing satisfaction at work for faculty in a large academic medical center in the United States.

Jasleen Grewal, BSc.
Genome Sciences Centre
British Columbia Cancer Research Centre
Vancouver, British Columbia, Canada
MedicalResearch.com: What is the background for this study?
Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain. We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy.
Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues.



Dr. Kendall[/caption]
Dr Kimberley Kendall MBBCh
Wellcome Trust Clinical Research Fellow
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