A characteristic function approach to the biased sampling model, with application to robust logistic regression

2008 ◽  
Vol 138 (3) ◽  
pp. 742-755 ◽  
Author(s):  
Howard D. Bondell
Keyword(s):  
Logistic Regression ◽  
Characteristic Function ◽  
Function Approach ◽  
Biased Sampling

Prevalence and predictors of burnout among specialty palliative care clinicians in the United States: Results of a national survey.

2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 87-87 ◽  
Author(s):  
Arif Kamal ◽  
Janet Bull ◽  
Steven Wolf ◽  
Greg Samsa ◽  
Katherine Ast ◽  
...  
Keyword(s):  
Palliative Care ◽  
Logistic Regression ◽  
Emotional Exhaustion ◽  
Cancer Care ◽  
Maslach Burnout Inventory ◽  
The United States ◽  
Biased Sampling ◽  
Stepwise Logistic Regression ◽  
Work Related ◽  
Increased Risk

87 Background: The increasing demand for specialist palliative care (PC) in cancer care requires an available and responsive clinical workforce. But as the volume of work expands, PC providers face an increased risk of burnout, characterized by depersonalization and work-related emotional exhaustion. The prevalence and predictors of burnout among PC providers is poorly understood. Methods: Members of the American Academy of Hospice and Palliative Medicine (e.g. advanced practice provider, registered nurses, chaplains) completed the Maslach Burnout Inventory (MBI) via electronic survey in the second half of 2013; respondents were recruited via e-mail, blog and Facebook posts, and Twitter tweets. The MBI determine burnout severity across two domains, Emotional Exhaustion (EE) and Depersonalization (DP). Severity is reported as “low”, “moderate”, or “high”; “high” is consistent with burnout. Demographic and work-related variables associated with burnout in other studies were determined via stepwise logistic regression. Results: We received surveys from 1,241 clinicians. We estimated a response rate of 30%. 68% of respondents were physicians. Most respondents were over age 50 (57%), female (65%), were married or partnered (82%) and had worked in the field for less than 10 years (67%). 42% took overnight call regularly, 30% reported working 50 hours per week or more, and 57% had at least 4 colleagues. Regarding burnout, 24% reported high DP, 59% reported high EE, and 62% of reported high burnout symptoms on either EE or DP scales. In logistic regression, younger physicians, those working more than 50 hours per work, and those with fewer colleagues within their practice were at greatest risk of burnout (p<0.02). Conclusions: PC clinicians report a burnout rate of 62%. Even with the limitations of our methods and potentially biased sampling, this prevalence is remarkable and higher than data reported in medical oncology (45%). Burnout severity is associated with working in isolation and working longer hours. Further studies on how burnout affects sustainability of the PC workforce are needed, especially since this workforce is so critical to the provision of high quality cancer care.

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