The Effect of Work Characteristics on Advanced Practice Provider Burnout: a secondary cross-sectional analysis

The Effect of Work Characteristics on Advanced Practice Provider Burnout: a secondary cross-sectional analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Work Characteristics on Advanced Practice Provider Burnout: a secondary cross-sectional analysis Danielle Miltz, Rachel Swerdlin, Heather Meiseen, Christopher Newman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8437047/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background : There are few comprehensive studies looking at burnout and well-being among advanced practice providers (APPs). Most of the studies focus on physicians and nurses. The prevalence of burnout in APPs is limited largely to single center studies or single type of APP specialty. Determining drivers of burnout amongst a heterogenous APP workforce may strengthen the ability to customize support and interventions. In a multicenter randomized control trial studying the feasibility and impact of online group coaching on APPs, we analyzed our baseline data to identify work characteristics associated with APP burnout. Methods : APPs from Emory University, Children's Healthcare of Atlanta, University of Colorado, and Children's Hospital Colorado who enrolled in an APP Coaching study completed a baseline survey which included demographics and Maslach Burnout Inventory Human Services Survey for Medical Personnel (MBI-HSS (MP)). Univariate and multivariate linear and ordinal regression were used for the subdomains of MBI. Univariate predicted probabilities were used for the latent burnout profiles. Results : Of 319 APPs, 305 completed baseline data. 65% (n=198) are Advanced Practice Registered Nurses (APRNs), 35% (n=107) Physician Assistants (PA-C), 53% (n=161) practice in adults, 47% (n=144) practice in pediatrics, 51% (n=157) from Georgia, 49% (n=148) from Colorado, 60% (n=181) work weekday shifts, 40% (n=123) work “off” (night/swing/weekend) shifts, 70% (n=212) work inpatient/ED/Urgent care and 30% (n=93) work outpatient, 35% (n=107) report 11 years’ experience. The mean emotional exhaustion (EE) score was 3.32 (SD 1.21). 27% of APPs (n=79) experienced burnout in one dimension and 9% (n=27) in two dimensions. After adjusting for confounding variable, APPs working “off” shifts have decreased personal achievement (PA), APPs who work full-time compared to part-time and PRN have lower EE, and APPs working in adult specialties have more EE and depersonalization (DP). Conclusions : The differences in factors contributing to APP burnout by unique work characteristics identified in this study suggest that a “one size fits all” approach may not be effective. Further studies are needed to determine if customized interventions for surgical and adult specialties, APPs who work “off” shifts, and APPs working part-time or PRN can reduce these disparities. Advanced Practice Providers (APP) burnout MBI well-being APRN PA-C Background Burnout in health care professionals includes emotional exhaustion and compassion fatigue(1-4) and decreases productivity, quality of life and team morale and increases turnover and medical errors(5). Despite that Advanced Practice Providers (Nurse Practitioners, Physician Assistants, Certified Registered Nurse Anesthetists, Certified Nurse Midwives, and Clinical Nurse Specialists) (APPs) are rapidly increasing in numbers to address health care demands(5-7), there are far less studies on APP burnout compared to physician and bedside nursing colleagues. APP burnout has unique contributors specific to APP demands such as time constraints, physician demands, limited support by superiors, lack of financial reward and role ambiguity (8, 9) and start as early as in APP training(10). Additionally, low job satisfaction reported in an empiric review across APPs has been suspected to contribute to turnover (11) which may cost organizations up to 100% of the yearly pay(12). However, there is limited understanding of the effect of specific APP work characteristics on burnout. Most studies evaluating APP burnout focus on a specific subspeciality, are either NPs or PA-Cs only, or are single center studies. Additionally, there is heterogeneity in defining burnout when using the MBI. Some studies will define burnout as requiring two domains (high Emotional Exhaustion and Depersonalization) (13, 14) while other studies use only one domain(15). Given healthcare shortages, understanding various drivers to burnout among the APP workforce may allow for meaningful ways to augment job satisfaction and decrease burnout in a strategic cost-effective approach(16). Studies have shown that primary care APPs have high rates of burnout(17, 18), female APPs have higher burnout when compared to male colleagues(18, 19), and younger career APPs have greater depersonalization (10). Supportive coworkers and leadership, opportunities for advancement, and work life balance have been shown to be protective against burnout in APPs (20). Gaps remain in the understanding of job characteristics contributing to burnout such as APPs working in adults versus pediatric populations or APPs in full-time positions versus part-time. Studies on bedside nurses show that nurses working in inpatient settings have greater burnout compared to outpatient settings(21, 22) and working with adult patients have greater burnout compared to working with pediatric patients(23). Risk factors for physician burnout included female gender, primary care practice, and less experience(24). It is not fully characterized if APPs will follow similar trends in burnout and well-being to physician or bedside nursing colleagues or if they will have completely independent variables contributing to burnout. In this study, we conducted a multi-centered randomized controlled trial of professional coaching on APPs as an intervention. We will discuss an analysis of the baseline data to identify any work characteristics associated with APP burnout. Methods Setting and Participants This study was a convenience sampling of advanced practice providers (nurse practitioners, physician assistants, certified midwife nurses, certified registered nurse anesthetists, anesthesiology assistants, and clinical nurse specialists) from Emory University Hospitals, Children’s Healthcare of Atlanta, University of Colorado School of Hospital, and Children’s Hospital Colorado. Participants were recruited by way of 4 emails over the course of a 6-week enrollment period, various APP Division meetings and printed flyers. All participants voluntarily enrolled in the study. Participants completed written consent. This study was approved by the Emory University Institutional Review Board (IRB) and registered on ClinicalTrials.gov (NCT05938556). Three hundred and nineteen participants enrolled in the study. These participants volunteered into a digital, positive psychology-based program group coaching program designed to improve the well-being of APPs. The randomized controlled trial was conducted from September 1, 2023, to December 31, 2023. Data Collection Baseline study data was collected July to September, 2023 and managed using REDCap electronic data capture tools hosted at 1,2 REDCap (Research Electronic Data Capture) which is a secure, web-based software platform. Demographic information included: age, gender, marital status, living arrangements, caregiver status, race, and information specific to their APP training, location, work shift and specialty. We conducted an analysis of the baseline burnout data. The primary outcome measure was burnout, as defined by the Maslach Burnout Inventory21 Human Services Survey for Medical Personnel (MBI-HSS MP), a 22-item measurement of worker burnout which assesses emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) domains. A license was purchased with permission to use the MBI. Possible scores range from 0-6 on a Likert scale for each item The exploratory outcome measured was burnout profiles as described by Leiter and Maslach 2 and scored according to MBI Appendix F 1 . Thresholds for each domains are defined as follows: The threshold for EE is calculated by taking the mean of EE and adding the 0.5 times the standard deviation of EE, DP and PA are defined similarly but are 1.25 and 0.1 times the standard deviation respectively. The following thresholds were then calculated: EE had a threshold of 3.41, DP had a threshold of 3.88, and PA had a threshold of 4.54 (Appendix 1). Latent profiles are then defined by having different combinations of threshold domains (Appendix 2). Categorization and data cleaning of Demographic, Provider, and Exposures: Profession was collapsed to Advanced Practice Nurses (Nurse Practitioner, Certified Midwife Nurse, Clinical Nurse Specialist) versus. PA-C. Those who indicated they were an Anesthesiologist Assistant or Certified Registered Nurse Anesthetist were removed (29 individuals) due to lack of power within these groups. Age groups were collapsed from 5-year age categories to 20-35, 36-50, and 50+. Years of experience (also 5 level year categories) were collapsed to 0-10 and 11+ years of experience. We categorized type of shift as weekday shifts and those with “off” shifts (combination or solely night, weekend or swing shifts). Hospital work setting was defined as anyone with inpatient, emergency department, and urgent care and outpatient work setting as ambulatory or clinic. Those with a surgical specialty (e.g., surgical services, cardiothoracic surgery, surgical specialties, etc.) were grouped together and all other non-surgical specialties were combined (e.g., urgent care, primary care outpatient, specialty care, etc.) Statistical Analysis Continuous variables were summarized using mean and standard deviations (SD) while categorical variables are displayed as counts and percentages (%). Baseline characteristics of demographic, provider, and outcomes are summarized in Tables 1 and 2. Group comparisons using Chi-squared test and t-test were only performed to narrow down our variables of interest and are not shown. Variables of interest that displayed clinically meaningful effect sizes and or were statistically significant after adjusting for multiple comparisons using (Bonferroni’s Correction) were analyzed in univariate regression (gender and age were therefore excluded from further analysis). All outcomes were considered. Due to the variety in our outcomes multiple regression techniques were utilized including Linear, Logistic, and Ordinal Regression. All outcomes and their associations with the variables of interest (Primary Caregiver Status (Any adult, child, elderly, etc.), Marital Status, Weekly Overtime, APP Type, Years of Experience, Surgical vs. Medical, Adult versus Pediatric, Shift Type, Work Setting, and State Georgia versus Colorado) were assessed independently in Univariate Regression. Univariate Linear Regression: The following outcomes were assessed using Univariate Linear Regression: EE, DP, PA. While DP and PA showed slight deviations from normality there were no major violations, and all other assumptions were satisfied Odel fit statistics were assessed with AIC (not shown). Beta’s (β) and 95% Confidence Intervals (95% CI) were displayed. Univariate Ordinal Regression: Burnout profiles were assessed using ordinal regression. P-values were based on Wald tests. Odds Ratios (OR) and 95% Confidence Intervals (95% CI) are provided. When evaluating burnout profiles, there was no major difference when evaluating all 5 profiles compared to when the three intermediate levels (overextended, disengaged, ineffective) were clustered compared to engaged and burned out. For greater power, we chose 3 profile analysis. Univariate Logistic Regression: Using the 3 burnout profiles we then performed logistic regression comparing Engaged vs. Strained/Burnout and Engaged/Strained vs. Burnout. ORs and 95% CI were provided alongside predicted probabilities (PP). Multivariate Regression: All exposures of interest were also considered as confounders. There were no issues of multicollinearity between exposures. Model selection was based on a priori selection methods and not statistical methods. Therefore, all suspected confounders were included. A p-value of less than <0.05 was considered statistically significant. All data cleaning and analysis was performed in R Statistical Software (v4.2.1; R Core Team 2022). Results Participants Of 318 participants enrolled, 305 participants completed the baseline demographic survey and 292 completed the full MBI. 198 (65%) were APRN while 107 (35%) were PA-C. Most participants identified as female while only 12 (4%) identified as male. Approximately half of the participants (n=154) were 36-50 years of age, 37% were 20-35 years of age (n=112), and 13% (n=39) were 51+years of age. Years of experience were broken into two categories, 1-10 years 107 (35%) and 11+ years 118 (39%) respondents, respectively. Geographical location was evenly spread with 148 (49%) participants located in Colorado. Most providers were medical (n=240, 79%) compared to surgical (n=65, 21%). Roughly half of the participants cared for adult patients, 60% worked day shift only (n=181) and the majority worked in a hospital setting (n=212) (Table 1). Table 1. Demographics and Provider Characteristics Table 2 demonstrates cohort median levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). The EE of the cohort was a mean of 3.32 (SD 1.21), DP was a mean of 1.85 (SD 1.26) and PA was a mean of 4.43 (SD 0.81). A total of 106 participants scored positively for either high EE and/or high DP, which represents 36.3% of the 292 participants who completed the full MBI Of the profiles, roughly a third were engaged (n=104), and 9% burned out (n=27). About half the participants were in the three intermediate profiles, labeled strained. These include a quarter (n=72) overextended, roughly a quarter ineffective (n=82) and 2% disengaged (n=7). Table 2. Profiles and median levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA) Univariate and multivariate regression Compared to APNs, PAs had greater DP and EE (0.32 (95% CI, 0.04,0.61)), 0.53 (95% CI, 0.24,0.83)), respectively). Medical specialties had lower DP (-0.43 (95% CI, -0.79, -0.07)) compared to surgical specialties. APPs who work full-time had lower PA (-0.32 (95% CI, -.59, -.05)) and lower EE (-0.52 (95% CI, -0.92, -0.12)) compared to part-Time/PRN APPs. APPs who worked “off” shifts had lower PA (0.32 (95% CI, -0.5,-0.13)). APPs who worked in hospital-based settings had lower PA compared (-0.32 (95% CI, -0.52,-0.13)) to those working in outpatient settings. APPs practicing in adult specialties had higher EE (0.56 (95% CI, 0.29, 0.83)) and DP (0.53 (95% CI, 0.24, 0.81)). Caregivers had lower DP compared to non-caregivers (-0.33 (95% CI, -0.62, -0.04)). Years' experience, marital status, working overtime, and location of work did not show a significant difference among any of the MBI subscales or strained and/or burnout profiles. When confounding variables are considered using an adjusted regression, the only significant differences are full time employees had lower EE (-0.42 (95% CI, -0.82,-0.02)), and lower PA (-0.33(95% CI, -0.60,-0.06)) (Table 3). APPs picking up weekly overtime shifts had greater PA (0.27 (95% CI, 0.00,0.54)) which was previously insignificant. APPs practicing in adult specialties had greater EE (0.38 (095% CI,.08,0.68)) and DP (0.33 (95% CI, 0.02,0.64)). APPs who work “off” shift employees had lower PA (-0.28 (95% CI, -0.49,-0.07)) compared to those who worked day shift. Table 3: Multivariate Analysis of Outcomes and Predictors Linear Regression Ordinal Regression Characteristics Emotional Exhaustion Depersonalization Personal Achievement Burnout Profiles (3- levels) Beta (95% CI) Beta (95% CI) Beta (95% CI) OR (95% CI) Primary Caregiver Not a Caregiver Caregiver 0.08 (-0.23, 0.39) -0.18 (-0.50, 0.14) 0.09 (-0.12, 0.29) 0.88 (0.52, 1.5) Marital Status Married/Engaged/Partnered Single/Divorced 0.23 (-0.10, 0.57) 0.10 (-0.24, 0.44) -0.02(-0.25,0.20) 1.0(0.6,1.8) Weekly Overtime Monthly/Yearly/Never Weekly 0.12 (-0.29, 0.53) -0.12 (-0.55, 0.30) 0.27 (0.00, 0.54)* 1.0 (0.50, 2.0) Employment Status Part-Time/PRN Full-Time -0.42 (-0.82, -0.02)* -0.17 (-0.59, 0.24) -0.33 (-0.60, -0.06)* 1.4 (0.71, 2.7) APP Type Advanced Practice Nurse Physician Assistant 0.17 (-0.14, 0.49) 0.30 (-0.02, 0.63) -0.04 (-0.25, 0.17) 1.2 (0.72, 2.1) Years of Experience 0-5 years 6+ years -0.02 (-0.33, 0.29) 0.06 (-0.26, 0.38) 0.12 (-0.09, 0.32) 0.92 (0.55, 1.5) Type of Specialty Surgical Field Medical Field -0.15 (-0.52, 0.22) -0.35 (-0.73, 0.03) 0.16 (-0.09, 0.40) 0.54 (0.29, 0.99)* Patient Population Pediatric Patient Population Adult Patient Population 0.38 (0.08, 0.68)* 0.33 (0.02, 0.64)* -0.05 (-0.25, 0.15) 1.7 (1.1, 2.9)* Work Shift Day Shift Off Shift 0.05 (-0.27, 0.37) 0.19 (-0.14, 0.51) -0.28 (-0.49, -0.07)* 2.0 (1.2, 3.5)* Work Setting Outpatient Hospital Based -0.26 (-0.60, 0.08) -0.20 (-0.55, 0.15) -0.13 (-0.36, 0.09) 0.76 (0.42, 1.3) State of Work Georgia Colorado 0.10 (-0.20, 0.39) 0.01 (-0.29, 0.32) 0.08 (-0.12, 0.28) 1.2 (0.72, 2.0) Abbreviations: CI= Confidence Interval, OR= Odds Ratio, PP= Predicted Probabilities *Indicates statically significant value APPs in medical specialties were 46% less likely to score higher on the continuum of profiles compared to surgical specialties, however after adjusting for confounding variables this becomes insignificant. APPs who work with adult populations are 2.1 (95% CI, 1.3,3.3) times more likely to score higher on the continuum compared to those who work in pediatric populations, which decreased to 1.7 times more likely after adjusting for confounders (95% CI, 1.1, 2.9). Compared to APPs who worked dayshift, those who work “off” shifts were 1.9 (95% CI, 1.2, 3.0) times more likely to score higher on the continuum and increased to 2 (95% CI, 1.2, 3.5) times more likely once controlling for confounders. Univariate Logistic Regression with Predicted Probabilities APPs practicing in medical subspecialities were 2.27 (95% CI, 1.19, 4.59) times more likely to be engaged than strain and/or burnout compared to APPs practicing in surgical specialties. APPs working with adult populations were 48% less likely to be engaged compared to strain and/or burnout and 2.79 (95% CI, 1.19, 7.32) times more likely to be burned out compared APPs working with pediatric populations. APPs working off shifts were 43% less likely to be engaged compared to strained and burnout and 2.35 (95% CI, 1.06,5.4) times more likely to be burned out compared to dayshift. Colorado participants were 39% more likely to be engaged than strained and/or burnout but did not experience greater burnout compared to Georgia (Table 4). Table 4. Univariate Predicted Probabilities obtained from Ordinal Regression and Logistic Regression Results of the Combined 3-level Burnout Profiles. Predictor Engaged Strained Burnout Engaged vs. Strained/Burnout Engaged/Strained vs Burnout N (%), (PP) N (%), (PP) N (%), (PP) OR (95% CI) OR (95% CI) Primary Caregiver Not a Caregiver 38 (32%), (0.31) 69 (57%), (0.58) 13 (11%), (0.1) Caregiver 65 (39%), (0.39) 90 (54%), (0.53) 12 97%), (0.08) 1.38 (0.84, 2.27) 0.64 (0.28,1.46) Marital Status Married/Engaged/Partnered 79 (37%), (0.36) 117 (54%), (0.55) 20 (9%), (0.09) Single/Divorced 25 (33%), (0.34) 44 (58%), (0.56) 7 (9%), (0.1) 0.85 (0.48, 1.47) 0.99 (0.38,2.35) Weekly Overtime Monthly/Yearly/Never 91 (36%), (0.36) 137 (54%), (0.55) 24 (10%), (0.09) Weekly 13 (32%), (0.34) 23 (57%), (0.57) 3 (8%), (0.1) 0.85 (0.41,1.7) 0.77 (0.18, 2.35) Employment Status Part-Time/PRN 91 (36%), (0.36) 137 (55%), (0.55) 23 (9%), (0.09) Full-Time 13 (32%), (0.33) 23 (57%), (0.57) 4 (10%), (0.1) 0.85 (0.4, 1.69) 1.1 (0.31, 3.07) APP Type Advanced Practice Nurse 74 (39%), (0.4) 102 (54%), (0.53) 13 (7%), (0.08) Physician Assistant 30 (29%), (0.28) 59 (57%), (0.6) 14 (14%), (0.12) 0.64 (0.38, 1.06) 2.13 (0.96, 4.78) Years of Experience 0-5 years 31 (34%), (0.34) 53 (58%), (0.56) 8 (9%), (0.1) 6+ years 73 (37%),(0.36) 107 (54%), (0.55) 19 (10%), (0.09) 1.14 (0.68, 1.93) 1.11 (0.48, 2.78) Type of Specialty Surgical Field 13 (22%), (0.25) 40 (68%), (0.61) 6 (10%), (0.14) Medical Field 91 (39%), (0.38) 121 (52%), (0.54) 21 (9%), (0.08) 2.27 (0.32,0.84)* 0.88 (0.35, 2.48) Patient Population Pediatric Patient Population 60 (43%), (0.44) 71 (51%), (0.5) 7 (5%), (0.06) Adult Patient Population 44 (29%), (0.28) 90 (58%), (0.6) 20 (13%), (0.12) 0.52 (0.32,0.84)* 2.79 (1.19, 7.32)* Work Shift Day Shift 71 (41%), (0.41) 92 (53%), (0.52) 11 (6%), (0.07) Off Shift 33 (28%), (0.27) 68 (58%), (0.6) 16 (14%), (0.13) 0.57 (0.34,0.94)* 2.35 (1.06, 5.4)* Work Setting Outpatient 35 (39%), (0.37) 45 (50%), (0.54) 10 (11%), (0.09) Hospital Based 69 (34%), (0.35) 116 (57%), (0.56) 17 (8%), (0.09) 0.82 (0.49, 1.37) 0.74 (0.33, 1.73) State of Work Georgia 62 (41%), (0.4) 75 (50%), (0.52) 14 (9%), (0.08) Colorado 42 (30%), (0.31) 86 (61%), (0.58) 13 (9%), (0.11) 0.61 (0.37, 0.99)* 0.99 (0.45, 2.21) Abbreviations: CI= Confidence Interval, OR= Odds Ratio, PP= Predicted Probabilities *Indicates statically significant value Discussion This multicenter study analyzed the baseline characteristics of a large APP coaching study to determine drivers of APP burnout. We report a prevalence of 36% burnout when measured in one dimension of either high EE or high DP. When using latent profiles, we report 9% of APPs were burned out and about half of the APPs are strained. Our study showed that APPs working in adult subspecialities experience more EE and DP. APPs working with adults and who work “off” shift are less likely to be engaged, score higher on the continuum between engaged and burned out and more likely to be burned out. APPs who work in medical fields are more likely to be engaged and less likely to progress along the continuum from engaged to burned out. While APPs who work full-time are less likely to experience PA, they also experience less EE. APPs who pick up weekly overtime are more likely to experience PA. Amongst the MBI subdomains, the cohort mean EE was similar to previous reports of 30–50% APP burnout, and similar to physicians(25). When evaluating a multidimensional experience, 55% of the cohort was in the strain category, having a sub-optimal experience. This is similar to other person-centric studies on healthcare provider's progression to burnout(13, 14). These nuances may inform designing intervention strategies according to the needs and characteristics of each type of burnout profile in a vulnerable strained group and may mitigate the progression to burnout. Working in adult specialties confers a risk towards burnout, which follows a similar trend that is seen in nursing(23) and physicians (26). While pediatrics contains chronic illness, those working in adult populations may experience greater co-morbidities, end-of-life discussions and challenges in patient autonomy with medical decision making. APPs who work off shifts had lower PA, were less likely to be engaged, were more likely to progress along the continuum of burn out, and more likely to be burned out. This has similar findings to both physician and bedside nurses who experienced greater burnout with nightshift across all three domains(27) and specifically lower PA in physicians who worked greater number of nightshifts(28). This may be due to decreased educational opportunities and career advancements available to “off” shift APPs, however further studies are needed to understand the specific challenges of “off” shift. Medical subspecialties were more engaged and less likely to progress along the burnout continuum compared to surgical subspecialties. This is consistent with previous reports of higher prevalence of burnout in surgical providers(29). APPs who work in surgical fields share work characteristics postulated to contribute to burnout in surgical nursing and physician, such as demanding, high-pressure situations, less predictable schedules, and often have more autonomy in their daily practice(29–31). Additionally full-time APPs are less EE and APPs who picked up frequent overtime shifts are more likely to have higher PA compared to part-time APPs and APPs who did not work frequent overtime shifts, respectively. This trend differs from physicians who experienced higher burnout with increased overtime shift(32, 33). An increase in burnout with overtime is not shown as clearly in nursing (34, 35). This suggests there could be a benefit of the regular cadence of practice for APPs and possibly lower stress from predictable job routine. Conclusions While healthcare professionals share many similar responsibilities, APPs have unique stressors specific to their roles. To our knowledge, this is the largest heterogenous description of APP work characteristics on burnout. Overall, APPs have moderate EE, similar prevalence as other nurses and physicians and when evaluated in a person-centered approach, over half that are strained. Similar to physician and nursing burnout literature, drivers of APP burnout include practicing in adult patient populations, surgical specialties and off shifts. Dissimilar to physicians and nurses, fulltime status decreases the likelihood of EE. Additionally, we characterized strained groups which may be most at risk for progression to burnout. These distinct profiles may suggest that a more customized approach should be taken to address the underlying problems for different groups. Abbreviations APPs- Advanced Practice Providers APRNs- Advanced Practice Registered Nurses PA-C- Physician Assistant MBI-HSS (MP)- Maslach Burnout Inventory Human Services Survey for Medical Personnel DP- Depersonalization EE- emotional exhaustion PA- Personal achievement IRB- Institutional Review Board REDCap- Research Electronic Data Capture SD- standard deviations Predicted Probability- PP Confidence Interval- CI Odds Ratio- OR Declarations Ethics approval and consent to participate- Written consent from all participants was obtained prior to being enrolled in the study. Emory University's Institutional Review Board (IRB) functions as its primary ethics review committee for human subjects’ research. Emory University's IRB is guided by the ethical principles as outlined by Belmont Report and, for clinical trials adheres to the Declaration of Helsinki and The International Council for Harmonisation (ICH) Good Clinical Practice guidelines. Consent for publication- Not applicable Availability of data and materials- The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. License to administer the MBI scale was obtained from Mind Garden. Competing interests- The authors declare they have no competing interests. Funding- Dudley Moore Research Grant and Children's Healthcare of Atlanta Authors' contributions DM- contributed to data interpretation and a major contributor in writing the manuscript RS- contributed to data interpretation and background HM- contributed to data interpretation CN- contributed to data interpretation AJ- analyzed data and contributed to data interpretation CC- study design and advisory ZR- contributed to data interpretation and a major contributor to writing the manuscript All authors read and approved of the submitted manuscript. References Maslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol. 2001;52:397-422. 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Critical Care Explorations. 2022;4(3):e0654. Valdes-Elizondo GD, Álvarez-Maldonado P, Ocampo-Ocampo MA, Hernández-Ríos G, Réding-Bernal A, Hernández-Solís A. Burnout symptoms among physicians and nurses before, during and after COVID-19 care. Rev Lat Am Enfermagem. 2023;31:e4046. Kansoun Z, Boyer L, Hodgkinson M, Villes V, Lançon C, Fond G. Burnout in French physicians: A systematic review and meta-analysis. J Affect Disord. 2019;246:132-47. Golisch KB, Sanders JM, Rzhetsky A, Tatebe LC. Addressing Surgeon Burnout Through a Multi-level Approach: A National Call to Action. Curr Trauma Rep. 2023;9(2):28-39. Senturk JC, Melnitchouk N. Surgeon Burnout: Defining, Identifying, and Addressing the New Reality. Clin Colon Rectal Surg. 2019;32(6):407-14. Jesuyajolu D, Nicholas A, Okeke C, Obi C, Aremu G, Obiekwe K, Obinna I. Burnout among surgeons and surgical trainees: A systematic review and meta-analysis of the prevalence and associated factors. Surg Pract Sci. 2022;10:100094. Küppers L, Göbel J, Aretz B, Rieger MA, Weltermann B. Associations between COVID-19 Pandemic-Related Overtime, Perceived Chronic Stress and Burnout Symptoms in German General Practitioners and Practice Personnel-A Prospective Study. Healthcare (Basel). 2024;12(4). Zana-Taïeb E, Kermorvant E, Beuchée A, Patkaï J, Rozé JC, Torchin H. Excessive workload and insufficient night-shift remuneration are key elements of dissatisfaction at work for French neonatologists. Acta Paediatr. 2023;112(10):2075-83. Bae SH. Nurse Staffing, Work Hours, Mandatory Overtime, and Turnover in Acute Care Hospitals Affect Nurse Job Satisfaction, Intent to Leave, and Burnout: A Cross-Sectional Study. Int J Public Health. 2024;69:1607068. Gómez-Urquiza JL, De la Fuente-Solana EI, Albendín-García L, Vargas-Pecino C, Ortega-Campos EM, Cañadas-De la Fuente GA. Prevalence of Burnout Syndrome in Emergency Nurses: A Meta-Analysis. Crit Care Nurse. 2017;37(5):e1-e9. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementalAPPMBI.docx floatimage1.png Table 1. Demographics and Provider Characteristics floatimage2.png Table 2. Profiles and median levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA) Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Editor invited by journal 11 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 09 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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13:12:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19150,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalAPPMBI.docx","url":"https://assets-eu.researchsquare.com/files/rs-8437047/v1/0ab0c711e98103fe2e0b3aaa.docx"},{"id":100592799,"identity":"70b0b01d-2d49-4c81-9904-ca260c51ca02","added_by":"auto","created_at":"2026-01-19 13:11:55","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":119629,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1. Demographics and Provider Characteristics\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8437047/v1/0b45003f901d6793e323c15f.png"},{"id":100592798,"identity":"d357ffe7-9ad1-4c4e-b2e4-d742f04e1c45","added_by":"auto","created_at":"2026-01-19 13:11:55","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":129788,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2. Profiles and median levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8437047/v1/b9d89cafe6d03de81166ddc7.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Effect of Work Characteristics on Advanced Practice Provider Burnout: a secondary cross-sectional analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eBurnout in health care professionals includes emotional exhaustion and compassion fatigue(1-4) and decreases productivity, quality of life and team morale and increases turnover and medical errors(5). Despite that Advanced Practice Providers (Nurse Practitioners, Physician Assistants, Certified Registered Nurse Anesthetists, Certified Nurse Midwives, and Clinical Nurse Specialists) (APPs) are rapidly increasing in numbers to address health care demands(5-7), there are far less studies on APP burnout compared to physician and bedside nursing colleagues. APP burnout has unique contributors specific to APP demands such as time constraints, physician demands, limited support by superiors, lack of financial reward and role ambiguity (8, 9) and start as early as in APP training(10). Additionally, low job satisfaction reported in an empiric review across APPs has been suspected to contribute to turnover (11) which may cost organizations up to 100% of the yearly pay(12). However, there is limited understanding of the effect of specific APP work characteristics on burnout. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost studies evaluating APP burnout focus on a specific subspeciality, are either NPs or PA-Cs only, or are single center studies. Additionally, there is heterogeneity in defining burnout when using the MBI. Some studies will define burnout as requiring two domains (high Emotional Exhaustion and Depersonalization) (13, 14) while other studies use only one domain(15). Given healthcare shortages, understanding various drivers to burnout among the APP workforce may allow for meaningful ways to augment job satisfaction and decrease burnout in a strategic cost-effective approach(16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies have shown that primary care APPs have high rates of burnout(17, 18), female APPs have higher burnout when compared to male colleagues(18, 19), and younger career APPs have greater depersonalization (10). Supportive coworkers and leadership, opportunities for advancement, and work life balance have been shown to be protective against burnout in APPs (20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGaps remain in the understanding of job characteristics contributing to burnout such as APPs working in adults versus pediatric populations or APPs in full-time positions versus part-time. Studies on bedside nurses show that nurses working in inpatient settings have greater burnout compared to outpatient settings(21, 22) and working with adult patients have greater burnout compared to working with pediatric patients(23). Risk factors for physician burnout included female gender, primary care practice, and less experience(24). \u0026nbsp; It is not fully characterized if APPs will follow similar trends in burnout and well-being to physician or bedside nursing colleagues or if they will have completely independent variables contributing to burnout.\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted a multi-centered randomized controlled trial of professional coaching on APPs as an intervention. We will discuss an analysis of the baseline data to identify any work characteristics associated with APP burnout.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eSetting and Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a convenience sampling of advanced practice providers (nurse practitioners, physician assistants, certified midwife nurses, certified registered nurse anesthetists, anesthesiology assistants, and clinical nurse specialists) from Emory University Hospitals, Children\u0026rsquo;s Healthcare of Atlanta, University of Colorado School of Hospital, and Children\u0026rsquo;s Hospital Colorado. Participants were recruited by way of 4 emails over the course of a 6-week enrollment period, various APP Division meetings and printed flyers. All participants voluntarily enrolled in the study. Participants completed written consent. This study was approved by the Emory University Institutional Review Board (IRB) and registered on ClinicalTrials.gov (NCT05938556). Three hundred and nineteen participants enrolled in the study. These participants volunteered into a digital, positive psychology-based program group coaching program designed to improve the well-being of APPs. \u0026nbsp; The randomized controlled trial was conducted from September 1, 2023, to December 31, 2023. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBaseline study data was collected July to September, 2023 and managed using REDCap electronic data capture tools hosted at\u003csup\u003e1,2\u003c/sup\u003e REDCap (Research Electronic Data Capture) which is a secure, web-based software platform. Demographic information included: age, gender, marital status, living arrangements, caregiver status, race, and information specific to their APP training, location, work shift and specialty. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe conducted an analysis of the baseline burnout data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The primary outcome measure was burnout, as defined by the Maslach Burnout Inventory21 Human Services Survey for Medical Personnel (MBI-HSS MP), a 22-item measurement of worker burnout which assesses emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) domains. A license was purchased with permission to use the MBI. Possible scores range from 0-6 on a Likert scale for each item\u003c/p\u003e\n\u003cp\u003eThe exploratory outcome measured was burnout profiles as described by Leiter and Maslach\u003csup\u003e2\u003c/sup\u003e and scored according to MBI Appendix F\u003csup\u003e1\u003c/sup\u003e. Thresholds for each domains are defined as follows: The threshold for EE is calculated by taking the mean of EE and adding the 0.5 times the standard deviation of EE, DP and PA are defined similarly but are 1.25 and 0.1 times the standard deviation respectively. The following thresholds were then calculated: EE had a threshold of 3.41, DP had a threshold of 3.88, and PA had a threshold of 4.54 (Appendix 1). Latent profiles are then defined by having different combinations of threshold domains (Appendix 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCategorization and data cleaning of Demographic, Provider, and Exposures: Profession was collapsed to Advanced Practice Nurses (Nurse Practitioner, Certified Midwife Nurse, Clinical Nurse Specialist) versus. PA-C. Those who indicated they were an Anesthesiologist Assistant or Certified Registered Nurse Anesthetist were removed (29 individuals) due to lack of power within these groups. Age groups were collapsed from 5-year age categories to 20-35, 36-50, and 50+. Years of experience (also 5 level year categories) were collapsed to 0-10 and 11+ years of experience. We categorized type of shift as weekday shifts and those with \u0026ldquo;off\u0026rdquo; shifts (combination or solely night, weekend or swing shifts). Hospital work setting was defined as anyone with inpatient, emergency department, and urgent care and outpatient work setting as ambulatory or clinic. Those with a surgical specialty (e.g., surgical services, cardiothoracic surgery, surgical specialties, etc.) were grouped together and all other non-surgical specialties were combined (e.g., urgent care, primary care outpatient, specialty care, etc.)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were summarized using mean and standard deviations (SD) while categorical variables are displayed as counts and percentages (%). Baseline characteristics of demographic, provider, and outcomes are summarized in Tables 1 and 2. Group comparisons using Chi-squared test and t-test were only performed to narrow down our variables of interest and are not shown. Variables of interest that displayed clinically meaningful effect sizes and or were statistically significant after adjusting for multiple comparisons using (Bonferroni\u0026rsquo;s Correction) were analyzed in univariate regression (gender and age were therefore excluded from further analysis). All outcomes were considered. Due to the variety in our outcomes multiple regression techniques were utilized including Linear, Logistic, and Ordinal Regression. All outcomes and their associations with the variables of interest (Primary Caregiver Status (Any adult, child, elderly, etc.), Marital Status, Weekly Overtime, APP Type, Years of Experience, Surgical vs. Medical, Adult versus Pediatric, Shift Type, Work Setting, and State Georgia versus Colorado) were assessed independently in Univariate Regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate Linear Regression: The following outcomes were assessed using Univariate Linear Regression: EE, DP, PA. While DP and PA showed slight deviations from normality there were no major violations, and all other assumptions were satisfied Odel fit statistics were assessed with AIC (not shown). Beta\u0026rsquo;s (\u0026beta;) and 95% Confidence Intervals (95% CI) were displayed.\u003c/p\u003e\n\u003cp\u003eUnivariate Ordinal Regression: Burnout profiles were assessed using ordinal regression. P-values were based on Wald tests. Odds Ratios (OR) and 95% Confidence Intervals (95% CI) are provided. \u0026nbsp; When evaluating burnout profiles, there was no major difference when evaluating all 5 profiles compared to when the three intermediate levels (overextended, disengaged, ineffective) were clustered compared to engaged and burned out. For greater power, we chose 3 profile analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate Logistic Regression: Using the 3 burnout profiles we then performed logistic regression comparing Engaged vs. Strained/Burnout and Engaged/Strained vs. Burnout. ORs and 95% CI were provided alongside predicted probabilities (PP).\u003c/p\u003e\n\u003cp\u003eMultivariate Regression: All exposures of interest were also considered as confounders. There were no issues of multicollinearity between exposures. Model selection was based on a priori selection methods and not statistical methods. Therefore, all suspected confounders were included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA p-value of less than \u0026lt;0.05 was considered statistically significant. All data cleaning and analysis was performed in R Statistical Software (v4.2.1; R Core Team 2022).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf 318 participants enrolled, 305 participants completed the baseline demographic survey and 292 completed the full MBI. 198 (65%) were APRN while 107 (35%) were PA-C. Most participants identified as female while only 12 (4%) identified as male. Approximately half of the participants (n=154) were 36-50 years of age, 37% were 20-35 years of age (n=112), and 13% (n=39) were 51+years of age. Years of experience were broken into two categories, 1-10 years 107 (35%) and 11+ years 118 (39%) respondents, respectively. Geographical location was evenly spread with 148 (49%) participants located in Colorado. Most providers were medical (n=240, 79%) compared to surgical (n=65, 21%). Roughly half of the participants cared for adult patients, 60% worked day shift only (n=181) and the majority worked in a hospital setting (n=212) (Table 1). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Demographics and Provider Characteristics\u003c/p\u003e\n\u003cp\u003eTable 2 demonstrates cohort median levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). The EE of the cohort was a mean of 3.32 (SD 1.21), DP was a mean of 1.85 (SD 1.26) and PA was a mean of 4.43 (SD 0.81). A total of 106 participants scored positively for either high EE and/or high DP, which represents 36.3% of the 292 participants who completed the full MBI Of the profiles, roughly a third were engaged (n=104), and 9% burned out (n=27). About half the participants were in the three intermediate profiles, labeled strained. These include a quarter (n=72) overextended, roughly a quarter ineffective (n=82) and 2% disengaged (n=7).\u003c/p\u003e\n\u003cp\u003eTable 2. Profiles and median levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUnivariate and multivariate regression\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to APNs, PAs had greater DP and EE (0.32 (95% CI, 0.04,0.61)), 0.53 (95% CI, 0.24,0.83)), respectively). \u0026nbsp; Medical specialties had lower DP (-0.43 (95% CI, -0.79, -0.07)) compared to surgical specialties. APPs who work full-time had lower PA (-0.32 (95% CI, -.59, -.05)) and lower EE (-0.52 (95% CI, -0.92, -0.12)) compared to part-Time/PRN APPs. APPs who worked \u0026ldquo;off\u0026rdquo; shifts had lower PA (0.32 (95% CI, -0.5,-0.13)). APPs who worked in hospital-based settings had lower PA compared (-0.32 (95% CI, -0.52,-0.13)) to those working in outpatient settings. APPs practicing in adult specialties had higher EE (0.56 (95% CI, 0.29, 0.83)) and DP (0.53 (95% CI, 0.24, 0.81)). Caregivers had lower DP compared to non-caregivers (-0.33 (95% CI, -0.62, -0.04)). Years\u0026apos; experience, marital status, working overtime, and location of work did not show a significant difference among any of the MBI subscales or strained and/or burnout profiles.\u003c/p\u003e\n\u003cp\u003eWhen confounding variables are considered using an adjusted regression, the only significant differences are full time employees had lower EE (-0.42 (95% CI, -0.82,-0.02)), and lower PA (-0.33(95% CI, -0.60,-0.06)) (Table 3). APPs picking up weekly overtime shifts had greater PA (0.27 (95% CI, 0.00,0.54)) which was previously insignificant. APPs practicing in adult specialties had greater EE (0.38 (095% CI,.08,0.68)) and DP (0.33 (95% CI, 0.02,0.64)). APPs who work \u0026ldquo;off\u0026rdquo; shift employees had lower PA (-0.28 (95% CI, -0.49,-0.07)) compared to those who worked day shift. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Multivariate Analysis of Outcomes and Predictors\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"667\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eLinear Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eOrdinal Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003eEmotional Exhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eDepersonalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePersonal Achievement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eBurnout Profiles (3- levels)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003eBeta (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eBeta (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003eBeta (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Caregiver\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eNot a Caregiver\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eCaregiver\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.08 (-0.23, 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.18 (-0.50, 0.14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.09 (-0.12, 0.29)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e0.88 (0.52, 1.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eMarried/Engaged/Partnered\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eSingle/Divorced\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.23 (-0.10, 0.57)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.10 (-0.24, 0.44)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.02(-0.25,0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1.0(0.6,1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeekly Overtime\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonthly/Yearly/Never\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eWeekly\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.12 (-0.29, 0.53)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.12 (-0.55, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.27 (0.00, 0.54)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.0 (0.50, 2.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003ePart-Time/PRN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eFull-Time\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.42 (-0.82, -0.02)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.17 (-0.59, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp; -0.33 (-0.60, -0.06)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.4 (0.71, 2.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPP Type\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdvanced Practice Nurse\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhysician Assistant\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.17 (-0.14, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.30 (-0.02, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp; -0.04 (-0.25, 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.2 (0.72, 2.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Experience\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003e0-5 years\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003e6+ years\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.02 (-0.33, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.06 (-0.26, 0.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.12 (-0.09, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.92 (0.55, 1.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Specialty\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eSurgical Field\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eMedical Field\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.15 (-0.52, 0.22)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.35 (-0.73, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.16 (-0.09, 0.40)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e0.54 (0.29, 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient Population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003ePediatric Patient Population\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdult Patient Population\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.38 (0.08, 0.68)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.33 (0.02, 0.64)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.05 (-0.25, 0.15)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e1.7 (1.1, 2.9)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Shift\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eDay Shift\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eOff Shift\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.05 (-0.27, 0.37)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.19 (-0.14, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp; -0.28 (-0.49, -0.07)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.0 (1.2, 3.5)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Setting\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eOutpatient\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHospital Based\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp; -0.26 (-0.60, 0.08)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.20 (-0.55, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp; -0.13 (-0.36, 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.76 (0.42, 1.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState of Work\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eGeorgia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eColorado\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.10 (-0.20, 0.39)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.01 (-0.29, 0.32)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.08 (-0.12, 0.28)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e1.2 (0.72, 2.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 396px;\"\u003e\n \u003cp\u003eAbbreviations: CI= Confidence Interval, OR= Odds Ratio, PP= Predicted Probabilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 271px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 223px;\"\u003e\n \u003cp\u003e*Indicates statically significant value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 271px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAPPs in medical specialties were 46% less likely to score higher on the continuum of profiles compared to surgical specialties, however after adjusting for confounding variables this becomes insignificant. APPs who work with adult populations are 2.1 (95% CI, 1.3,3.3) times more likely to score higher on the continuum compared to those who work in pediatric populations, which decreased to 1.7 times more likely after adjusting for confounders (95% CI, 1.1, 2.9). Compared to APPs who worked dayshift, those who work \u0026ldquo;off\u0026rdquo; shifts were 1.9 (95% CI, 1.2, 3.0) times more likely to score higher on the continuum and increased to 2 (95% CI, 1.2, 3.5) times more likely once controlling for confounders. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUnivariate Logistic Regression with Predicted Probabilities\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAPPs practicing in medical subspecialities were 2.27 (95% CI, 1.19, 4.59) times more likely to be engaged than strain and/or burnout compared to APPs practicing in surgical specialties. APPs working with adult populations were 48% less likely to be engaged compared to strain and/or burnout and 2.79 (95% CI, 1.19, 7.32) times more likely to be burned out compared APPs working with pediatric populations. APPs working off shifts were 43% less likely to be engaged compared to strained and burnout and 2.35 (95% CI, 1.06,5.4) times more likely to be burned out compared to dayshift. Colorado participants were 39% more likely to be engaged than strained and/or burnout but did not experience greater burnout compared to Georgia (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. \u0026nbsp;Univariate Predicted Probabilities obtained from Ordinal Regression and Logistic Regression Results of the Combined 3-level Burnout Profiles.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eEngaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eStrained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eBurnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eEngaged vs. Strained/Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eEngaged/Strained vs Burnout\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eN (%), (PP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN (%), (PP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN (%), (PP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Caregiver\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eNot a Caregiver\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e38 (32%), (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e69 (57%), (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (11%), (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eCaregiver\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e65 (39%), (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e90 (54%), (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e12 97%), (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.38 (0.84, 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.64 (0.28,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eMarried/Engaged/Partnered\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e79 (37%), (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e117 (54%), (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20 (9%), (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eSingle/Divorced\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e25 (33%), (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e44 (58%), (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (9%), (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.85 (0.48, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.99 (0.38,2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeekly Overtime\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonthly/Yearly/Never\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e91 (36%), (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e137 (54%), (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e24 (10%), (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eWeekly\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (32%), (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e23 (57%), (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (8%), (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.85 (0.41,1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.77 (0.18, 2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003ePart-Time/PRN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e91 (36%), (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e137 (55%), (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e23 (9%), (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eFull-Time\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (32%), (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e23 (57%), (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (10%), (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.85 (0.4, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.1 (0.31, 3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPP Type\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdvanced Practice Nurse\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e74 (39%), (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e102 (54%), (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (7%), (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhysician Assistant\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e30 (29%), (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e59 (57%), (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e14 (14%), (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.64 (0.38, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.13 (0.96, 4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Experience\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003e0-5 years\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e31 (34%), (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e53 (58%), (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (9%), (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003e6+ years\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e73 (37%),(0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e107 (54%), (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e19 (10%), (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.14 (0.68, 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.11 (0.48, 2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Specialty\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eSurgical Field\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (22%), (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e40 (68%), (0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6 (10%), (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eMedical Field\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e91 (39%), (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e121 (52%), (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e21 (9%), (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.27 (0.32,0.84)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.88 (0.35, 2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient Population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003ePediatric Patient Population\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e60 (43%), (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e71 (51%), (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (5%), (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdult Patient Population\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e44 (29%), (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e90 (58%), (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e20 (13%), (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.52 (0.32,0.84)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.79 (1.19, 7.32)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Shift\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eDay Shift\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e71 (41%), (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e92 (53%), (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e11 (6%), (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eOff Shift\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e33 (28%), (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e68 (58%), (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e16 (14%), (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.57 (0.34,0.94)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.35 (1.06, 5.4)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Setting\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eOutpatient\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e35 (39%), (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e45 (50%), (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10 (11%), (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003eHospital Based\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e69 (34%), (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e116 (57%), (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e17 (8%), (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.82 (0.49, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.74 (0.33, 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState of Work\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eGeorgia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e62 (41%), (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e75 (50%), (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14 (9%), (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eColorado\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e42 (30%), (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e86 (61%), (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (9%), (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.61 (0.37, 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.99 (0.45, 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 396px;\"\u003e\n \u003cp\u003eAbbreviations: CI= Confidence Interval, OR= Odds Ratio, PP= Predicted Probabilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 99.827%;\"\u003e\n \u003cp\u003e*Indicates statically significant value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multicenter study analyzed the baseline characteristics of a large APP coaching study to determine drivers of APP burnout. We report a prevalence of 36% burnout when measured in one dimension of either high EE or high DP. When using latent profiles, we report 9% of APPs were burned out and about half of the APPs are strained. Our study showed that APPs working in adult subspecialities experience more EE and DP. APPs working with adults and who work \u0026ldquo;off\u0026rdquo; shift are less likely to be engaged, score higher on the continuum between engaged and burned out and more likely to be burned out. APPs who work in medical fields are more likely to be engaged and less likely to progress along the continuum from engaged to burned out. While APPs who work full-time are less likely to experience PA, they also experience less EE. APPs who pick up weekly overtime are more likely to experience PA.\u003c/p\u003e \u003cp\u003eAmongst the MBI subdomains, the cohort mean EE was similar to previous reports of 30\u0026ndash;50% APP burnout, and similar to physicians(25). When evaluating a multidimensional experience, 55% of the cohort was in the strain category, having a sub-optimal experience. This is similar to other person-centric studies on healthcare provider's progression to burnout(13, 14). These nuances may inform designing intervention strategies according to the needs and characteristics of each type of burnout profile in a vulnerable strained group and may mitigate the progression to burnout.\u003c/p\u003e \u003cp\u003eWorking in adult specialties confers a risk towards burnout, which follows a similar trend that is seen in nursing(23) and physicians (26). While pediatrics contains chronic illness, those working in adult populations may experience greater co-morbidities, end-of-life discussions and challenges in patient autonomy with medical decision making.\u003c/p\u003e \u003cp\u003eAPPs who work off shifts had lower PA, were less likely to be engaged, were more likely to progress along the continuum of burn out, and more likely to be burned out. This has similar findings to both physician and bedside nurses who experienced greater burnout with nightshift across all three domains(27) and specifically lower PA in physicians who worked greater number of nightshifts(28). This may be due to decreased educational opportunities and career advancements available to \u0026ldquo;off\u0026rdquo; shift APPs, however further studies are needed to understand the specific challenges of \u0026ldquo;off\u0026rdquo; shift.\u003c/p\u003e \u003cp\u003eMedical subspecialties were more engaged and less likely to progress along the burnout continuum compared to surgical subspecialties. This is consistent with previous reports of higher prevalence of burnout in surgical providers(29). APPs who work in surgical fields share work characteristics postulated to contribute to burnout in surgical nursing and physician, such as demanding, high-pressure situations, less predictable schedules, and often have more autonomy in their daily practice(29\u0026ndash;31). Additionally full-time APPs are less EE and APPs who picked up frequent overtime shifts are more likely to have higher PA compared to part-time APPs and APPs who did not work frequent overtime shifts, respectively. This trend differs from physicians who experienced higher burnout with increased overtime shift(32, 33). An increase in burnout with overtime is not shown as clearly in nursing (34, 35). This suggests there could be a benefit of the regular cadence of practice for APPs and possibly lower stress from predictable job routine.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWhile healthcare professionals share many similar responsibilities, APPs have unique stressors specific to their roles. To our knowledge, this is the largest heterogenous description of APP work characteristics on burnout. Overall, APPs have moderate EE, similar prevalence as other nurses and physicians and when evaluated in a person-centered approach, over half that are strained. Similar to physician and nursing burnout literature, drivers of APP burnout include practicing in adult patient populations, surgical specialties and off shifts. Dissimilar to physicians and nurses, fulltime status decreases the likelihood of EE. Additionally, we characterized strained groups which may be most at risk for progression to burnout. These distinct profiles may suggest that a more customized approach should be taken to address the underlying problems for different groups.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPPs- Advanced Practice Providers\u003c/p\u003e\n\u003cp\u003eAPRNs- Advanced Practice Registered Nurses\u003c/p\u003e\n\u003cp\u003ePA-C- Physician Assistant\u003c/p\u003e\n\u003cp\u003eMBI-HSS (MP)- Maslach Burnout Inventory Human Services Survey for Medical Personnel\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDP- Depersonalization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEE- emotional exhaustion\u003c/p\u003e\n\u003cp\u003ePA- Personal achievement\u003c/p\u003e\n\u003cp\u003eIRB- Institutional Review Board\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eREDCap- Research Electronic Data Capture\u003c/p\u003e\n\u003cp\u003eSD- standard deviations\u003c/p\u003e\n\u003cp\u003ePredicted Probability- PP\u003c/p\u003e\n\u003cp\u003eConfidence Interval- CI\u003c/p\u003e\n\u003cp\u003eOdds Ratio- OR\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate- Written\u003c/strong\u003e consent from all participants was obtained prior to being enrolled in the study. Emory University\u0026apos;s Institutional Review Board (IRB) functions as its primary ethics review committee for human subjects\u0026rsquo; research. Emory University\u0026apos;s IRB is guided by the ethical principles as outlined by Belmont Report and, for clinical trials adheres to the Declaration of Helsinki and The International Council for Harmonisation (ICH) Good Clinical Practice guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication-\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials- \u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. License to administer the MBI scale was obtained from Mind Garden. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests- \u003c/strong\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding-\u003c/strong\u003e Dudley Moore Research Grant and Children\u0026apos;s Healthcare of Atlanta\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDM- contributed to data interpretation and a major contributor in writing the manuscript\u003c/p\u003e\n\u003cp\u003eRS- contributed to data interpretation and background\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHM- contributed to data interpretation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCN- contributed to data interpretation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAJ- analyzed data and contributed to data interpretation\u003c/p\u003e\n\u003cp\u003eCC- study design and advisory\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZR- contributed to data\u0026nbsp;interpretation and a major contributor to writing the manuscript\u003c/p\u003e\n\u003cp\u003eAll authors read and approved of the submitted manuscript. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol. 2001;52:397-422.\u003c/li\u003e\n\u003cli\u003eSquires A, Finlayson C, Gerchow L, Cimiotti JP, Matthews A, Schwendimann R, et al. Methodological considerations when translating \u0026quot;burnout\u0026quot;. Burn Res. 2014;1(2):59-68.\u003c/li\u003e\n\u003cli\u003eHoukes I, Winants Y, Twellaar M, Verdonk P. Development of burnout over time and the causal order of the three dimensions of burnout among male and female GPs. A three-wave panel study. BMC Public Health. 2011;11:240.\u003c/li\u003e\n\u003cli\u003eSpataro BM, Tilstra SA, Rubio DM, McNeil MA. The Toxicity of Self-Blame: Sex Differences in Burnout and Coping in Internal Medicine Trainees. J Womens Health (Larchmt). 2016;25(11):1147-52.\u003c/li\u003e\n\u003cli\u003eKapu AN, Borg Card E, Jackson H, Kleinpell R, Kendall J, Lupear BK, et al. Assessing and addressing practitioner burnout: Results from an advanced practice registered nurse health and well-being study. 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Physician assistant burnout, job satisfaction, and career flexibility in Minnesota. Jaapa. 2019;32(7):41-7.\u003c/li\u003e\n\u003cli\u003eSeitz R, Robertson J, Moran TP, Zdradzinski MJ, Kaltiso SO, Heron S, Lall MD. Emergency Medicine Nurse Practitioner and Physician Assistant Burnout, Perceived Stress, and Utilization of Wellness Resources During 2020 in a Large Urban Medical Center. Adv Emerg Nurs J. 2022;44(1):63-73.\u003c/li\u003e\n\u003cli\u003eRazai MS, Kooner P, Majeed A. Strategies and Interventions to Improve Healthcare Professionals\u0026apos; Well-Being and Reduce Burnout. J Prim Care Community Health. 2023;14:21501319231178641.\u003c/li\u003e\n\u003cli\u003eDall\u0026apos;Ora C, Ball J, Reinius M, Griffiths P. Burnout in nursing: a theoretical review. Hum Resour Health. 2020;18(1):41.\u003c/li\u003e\n\u003cli\u003eShah MK, Gandrakota N, Cimiotti JP, Ghose N, Moore M, Ali MK. Prevalence of and Factors Associated With Nurse Burnout in the US. JAMA Netw Open. 2021;4(2):e2036469.\u003c/li\u003e\n\u003cli\u003eBuckley L, Berta W, Cleverley K, Medeiros C, Widger K. What is known about paediatric nurse burnout: a scoping review. Hum Resour Health. 2020;18(1):9.\u003c/li\u003e\n\u003cli\u003eOrtega MV, Hidrue MK, Lehrhoff SR, Ellis DB, Sisodia RC, Curry WT, et al. Patterns in Physician Burnout in a Stable-Linked Cohort. JAMA Network Open. 2023;6(10):e2336745-e.\u003c/li\u003e\n\u003cli\u003eAbraham CM, Zheng K, Norful AA, Ghaffari A, Liu J, Poghosyan L. Primary care nurse practitioner burnout and perceptions of quality of care. Nurs Forum. 2021;56(3):550-9.\u003c/li\u003e\n\u003cli\u003eDennis D, van Heerden P, Khanna R, Knott C, Zhang S, Calhoun A. The Different Challenges in Being an Adult Versus a Pediatric Intensivist. Critical Care Explorations. 2022;4(3):e0654.\u003c/li\u003e\n\u003cli\u003eValdes-Elizondo GD, \u0026Aacute;lvarez-Maldonado P, Ocampo-Ocampo MA, Hern\u0026aacute;ndez-R\u0026iacute;os G, R\u0026eacute;ding-Bernal A, Hern\u0026aacute;ndez-Sol\u0026iacute;s A. Burnout symptoms among physicians and nurses before, during and after COVID-19 care. Rev Lat Am Enfermagem. 2023;31:e4046.\u003c/li\u003e\n\u003cli\u003eKansoun Z, Boyer L, Hodgkinson M, Villes V, Lan\u0026ccedil;on C, Fond G. Burnout in French physicians: A systematic review and meta-analysis. J Affect Disord. 2019;246:132-47.\u003c/li\u003e\n\u003cli\u003eGolisch KB, Sanders JM, Rzhetsky A, Tatebe LC. Addressing Surgeon Burnout Through a Multi-level Approach: A National Call to Action. Curr Trauma Rep. 2023;9(2):28-39.\u003c/li\u003e\n\u003cli\u003eSenturk JC, Melnitchouk N. Surgeon Burnout: Defining, Identifying, and Addressing the New Reality. Clin Colon Rectal Surg. 2019;32(6):407-14.\u003c/li\u003e\n\u003cli\u003eJesuyajolu D, Nicholas A, Okeke C, Obi C, Aremu G, Obiekwe K, Obinna I. Burnout among surgeons and surgical trainees: A systematic review and meta-analysis of the prevalence and associated factors. Surg Pract Sci. 2022;10:100094.\u003c/li\u003e\n\u003cli\u003eK\u0026uuml;ppers L, G\u0026ouml;bel J, Aretz B, Rieger MA, Weltermann B. Associations between COVID-19 Pandemic-Related Overtime, Perceived Chronic Stress and Burnout Symptoms in German General Practitioners and Practice Personnel-A Prospective Study. Healthcare (Basel). 2024;12(4).\u003c/li\u003e\n\u003cli\u003eZana-Ta\u0026iuml;eb E, Kermorvant E, Beuch\u0026eacute;e A, Patka\u0026iuml; J, Roz\u0026eacute; JC, Torchin H. Excessive workload and insufficient night-shift remuneration are key elements of dissatisfaction at work for French neonatologists. Acta Paediatr. 2023;112(10):2075-83.\u003c/li\u003e\n\u003cli\u003eBae SH. Nurse Staffing, Work Hours, Mandatory Overtime, and Turnover in Acute Care Hospitals Affect Nurse Job Satisfaction, Intent to Leave, and Burnout: A Cross-Sectional Study. Int J Public Health. 2024;69:1607068.\u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez-Urquiza JL, De la Fuente-Solana EI, Albend\u0026iacute;n-Garc\u0026iacute;a L, Vargas-Pecino C, Ortega-Campos EM, Ca\u0026ntilde;adas-De la Fuente GA. Prevalence of Burnout Syndrome in Emergency Nurses: A Meta-Analysis. Crit Care Nurse. 2017;37(5):e1-e9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Advanced Practice Providers (APP), burnout, MBI, well-being, APRN, PA-C","lastPublishedDoi":"10.21203/rs.3.rs-8437047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8437047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: There are few comprehensive studies looking at burnout and well-being among advanced practice providers (APPs). Most of the studies focus on physicians and nurses. The prevalence of burnout in APPs is limited largely to single center studies or single type of APP specialty. Determining drivers of burnout amongst a heterogenous APP workforce may strengthen the ability to customize support and interventions. In a multicenter randomized control trial studying the feasibility and impact of online group coaching on APPs, we analyzed our baseline data to identify work characteristics associated with APP burnout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: APPs from Emory University, Children's Healthcare of Atlanta, University of Colorado, and Children's Hospital Colorado who enrolled in an APP Coaching study completed a baseline survey which included demographics and Maslach Burnout Inventory Human Services Survey for Medical Personnel (MBI-HSS (MP)). Univariate and multivariate linear and ordinal regression were used for the subdomains of MBI. Univariate predicted probabilities were used for the latent burnout profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Of 319 APPs, 305 completed baseline data. 65% (n=198) are Advanced Practice Registered Nurses (APRNs), 35% (n=107) Physician Assistants (PA-C), 53% (n=161) practice in adults, 47% (n=144) practice in pediatrics, 51% (n=157) from Georgia, 49% (n=148) from Colorado, 60% (n=181) work weekday shifts, 40% (n=123) work “off” (night/swing/weekend) shifts, 70% (n=212) work inpatient/ED/Urgent care and 30% (n=93) work outpatient, 35% (n=107) report \u0026lt; 10 years’ experience and 39% (n=118) report \u0026gt; 11 years’ experience. The mean emotional exhaustion (EE) score was 3.32 (SD 1.21). 27% of APPs (n=79) experienced burnout in one dimension and 9% (n=27) in two dimensions. After adjusting for confounding variable, APPs working “off” shifts have decreased personal achievement (PA), APPs who work full-time compared to part-time and PRN have lower EE, and APPs working in adult specialties have more EE and depersonalization (DP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The differences in factors contributing to APP burnout by unique work characteristics identified in this study suggest that a “one size fits all” approach may not be effective. Further studies are needed to determine if customized interventions for surgical and adult specialties, APPs who work “off” shifts, and APPs working part-time or PRN can reduce these disparities.\u003c/p\u003e","manuscriptTitle":"The Effect of Work Characteristics on Advanced Practice Provider Burnout: a secondary cross-sectional analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 13:04:55","doi":"10.21203/rs.3.rs-8437047/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T08:02:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T17:00:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T15:44:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253596857365662710282673357788352085507","date":"2026-01-16T13:26:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54326665713308350958895289824105464761","date":"2026-01-16T11:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139356112854376458473909050278439194460","date":"2026-01-14T19:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T06:54:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T06:47:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-12T04:42:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T14:40:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-01-09T14:31:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad67ae84-99aa-411a-9576-fd1b4e438e14","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:42:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 13:04:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8437047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8437047","identity":"rs-8437047","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}