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. 2016 Sep 12;51(5):1706–1734. doi: 10.1111/1475-6773.12499

Resilience among Employed Physicians and Mid‐Level Practitioners in Upstate New York

Anthony C Waddimba 1,2,✉, Melissa Scribani 3, Melinda A Hasbrouck 1, Nicole Krupa 3, Paul Jenkins 3, John J May 1,4
PMCID: PMC5034205  PMID: 27620116

Abstract

Objective

To investigate the factors associated with resilience among medical professionals.

Data Sources/Study Setting

Administrative information from a rural health care network (1 academic medical center, 6 hospitals, 31 clinics, and 20 school health centers) was triangulated with self‐report data from 308 respondents (response rate = 65.1 percent) to a 9/2013–1/2014 survey among practitioners serving a nine‐county 5,600‐square‐mile area.

Study Design

A cross‐sectional questionnaire survey comprising valid measures of resilience, practice meaningfulness, satisfaction, and risk/uncertainty intolerance, nested within a prospective, community‐based project.

Data Collection/Extraction Methods

The sampling frame included practitioners on institutional payroll, excluding voluntary/involuntary attritions and advisory board/research team members. In multivariable mixed‐effects models, we regressed full‐range and high‐/low‐resilience scores on demographics, professional satisfaction, workplace needs, risk/uncertainty intolerance, and service unit characteristics.

Principal Findings

Relational needs, uncertainty intolerance, satisfaction ≥75 percent of the time, number of practitioners on a unit, and workload were significantly associated with resilience. Higher scores were most strongly associated with uncertainty tolerance, satisfaction, and practitioner numbers. Practitioner/unit demographics were mostly nonsignificant.

Conclusions

More resilient practitioners experienced frequent satisfaction, relational needs gratification, better uncertainty tolerance, lighter workloads, and practiced on units with more colleagues. Further studies should investigate well‐being interventions based on these mutable factors.

Keywords: Stress resilience, satisfaction with practice, physicians, nurse practitioners, physician assistants

The rapid reform sweeping across the health system is characterized by escalating demands by various stakeholders for clinicians to be held accountable for the quality, safety, cost, and satisfactoriness of patient care (Friedberg et al. 2013; Tilburt et al. 2013; Goodman et al. 2014; Woolhandler and Himmelstein 2014; Anhang Price et al. 2015). Practitioners report more being demanded from them, with less autonomy being granted (Emmanuel and Pearson 2012) and fewer resources made available (Goodman et al. 2014). This paradoxical trend has heightened anxiety/stress among clinicians, which could endanger health care reforms (Dyrbye and Shanafelt 2011). Medical practitioners in the United States suffer higher rates of burnout (Shanafelt et al. 2012), suicidal ideation, and suicide (Gold, Sen, and Schwenk 2013) than other adult workers. In one study, 45 percent of a nationally representative sample of physicians had symptoms of burnout (Shanafelt et al. 2012).

The positive psychology movement (Lopez, Pedrotti, and Snyder 2014) is inspiring a shift away from emphasizing what ails medical professionals to a focus on what aids their coping with or adaptation to stressful changes, thereby strengthening their resilience (Yamey and Wilkes 2001; Eckleberry‐Hunt et al. 2009; Zwack and Schweitzer 2013). Instead of pathogenesis,1 this approach emphasizes salutogenesis 2 (Rabin et al. 2005; Lundman et al. 2010; Bringsén et al. 2012). This has sparked interest in stress resilience (McCann et al. 2013). Stressors within the clinical workplace can cause vicarious trauma, compassion fatigue, and burnout, but clinicians who successfully adapt and cope actually grow and thrive from the experience (Haglund et al. 2009; Taku 2014). Training practitioners in healthy ways of coping with stressors could thus optimize resilience (van der Kleij, Molenaar, and Schraagen 2011; Sood et al. 2011, 2014; Longenecker, Zink, and Florence 2012). Practitioners in rural/underserved areas are especially at risk of burnout, due to stressors that are unique to or accentuated in such settings (Chipp et al. 2011; Stevenson, Phillips, and Anderson 2011; Longenecker, Zink, and Florence 2012). Building resilience among rural practitioners enhances their well‐being (Longenecker, Zink, and Florence 2012; Morley 2012), thereby promoting workforce retention and access to care among nonmetropolitan populations (Haggerty et al. 2013). Further research into factors that facilitate or undermine resilience is necessary for the evidence‐based design of wellness interventions.

Although numerous studies address aspects of clinician well‐being, such as job satisfaction or burnout, few studies have examined stress resilience among rural practitioners responding to the contemporary era of reforms. Our purpose in this study was to investigate factors associated with resilience among medical professionals employed by an integrated health care network in a nine‐county rural region of upstate New York during a time of institutional and policy reforms. We also aimed to assess whether specific factors were more significantly associated with high versus low resilience, and vice versa, to uncover distinctions, if any, between facilitators and suppressors of resilience. Unlike many health workforce studies, we did not confine participation to nurse practitioners or physician assistants or physicians alone, but included practitioners from diverse disciplines, housed in many different departments, and dispersed over a vast geographic area. Health care is increasingly practiced by teams of professionals from diverse disciplines and varying ranks serving collaboratively in the same setting (Peterson et al. 2013). How clinical work affects practitioners together, across disciplinary lines, merits further scientific enquiry.

Conceptual Framework

We conceptualized resilience as the extent to which individuals positively cope with work stress or adversity by adapting effectively, bouncing back from it, and maintaining or enhancing their well‐being (Coutu 2002; Jensen et al. 2008; Epstein and Krasner 2013; McCann et al. 2013). Reasons why some practitioners develop resilience while others burn out are likely multifactorial (Taku 2014), with personal, team/group, and organizational factors playing a role. We framed resilience as a developable capacity rooted in personal resources (capacities, competencies, or attributes) or social factors (e.g., relational needs) enabling practitioners to cope with, adapt to, even thrive from, challenges (Caza and Milton 2012) of patient care in the midst of organizational transition. Organizations should simultaneously shelter resilience, by mitigating risks, and grow resilience, by capitalizing on assets (Luthans, Vogelgesang, and Lester 2006). Our conceptual model contrasted factors that undermine/erode resilience (risks) with those that build/buffer resilience (assets).

The Broaden and Build theory holds that individuals who frequently activate positive emotions build greater resilience (Fredrickson 2001; Tugade and Fredrickson 2004; Tugade, Fredrickson, and Barrett 2004). Positive states of mind buffer against negative effects of stress (Bränström 2013), facilitating adaptive coping (Gloria and Steinhardt 2016). Happiness with work builds resilience by fostering positive feelings about ordinary tasks (Coutu 2002). Since feelings have a tendency of fluctuating between joy and frustration, we focused on the proportion of time that a practitioner felt dis/satisfied. We hypothesized a positive association between resilience and frequent happiness with work. Focusing on aspects of practice that physicians find most meaningful (e.g., patient care, research, education) is inversely associated with burnout risk (Shanafelt et al. 2009). Meaningfulness of work is linked to hardiness, a construct similar to resilience, in other high‐stress professions (Britt, Adler, and Bartone 2001). The extent to which clinicians derive meaning from helping patients to heal was found, by prior studies, to correlate inversely with burnout and positively with resilience (Geller et al. 2008; Cooke, Doust, and Steele 2013). We hypothesized a positive association between resilience and the intrinsic meaning that practitioners derive from patient care.

Tolerance for uncertainty/ambiguity is reported to facilitate resilience (Cooke, Doust, and Steele 2013). Professionals with a high tolerance for uncertainty perceive ambiguous scenarios as desirable or intellectually stimulating (Herman et al. 2010). Health care is full of uncertainties and practitioners who perceive them as opportunities to grow can enhance their tolerance further (Geller 2013; Hancock et al. 2015). Those who are intolerant tend to avoid ambiguous stimuli, further undermining their ambiguity‐processing capacity (Geller 2013; Hancock et al. 2015). High‐reliability organizations promote resilience by enhancing employees' capacity to manage uncertainty (Weick and Sutcliffe 2015). We hypothesized that uncertainty and risk intolerance were negatively associated with resilience. Emerging evidence implicates excessive workloads in undermining practitioner morale (Huby et al. 2002), clinical proficiency (Elliott et al. 2014), and work–home balance (Keeton et al. 2007), thus worsening vulnerability to burnout (Dyrbye et al. 2011). We hypothesized that perceived workload is negatively associated with resilience.

Need‐based theories of motivation propose that the degree to which work gratifies individuals' psychosocial needs influences their occupational well‐being (Pinder 2008). Self‐determination theory, for instance, portrays work (like all volitional behavior) as motivated by universal needs for autonomy, relatedness, and achievement (Deci and Ryan 2008). We focused specifically on needs for autonomy and relatedness/affiliation in our study. We hypothesized that resilience was positively associated with autonomy and relational needs fulfillment. Finally, we incorporated contextual factors like practitioner demographics and practice unit characteristics in our model.

In summary, we hypothesized that resilience is significantly associated with psychosocial resources (such as needs for autonomy and relatedness, meaningfulness of work, tolerance for risk or uncertainty/ambiguity, and frequency of happiness with practice) and with practice unit variables (e.g., staffing levels) but not individual practitioner demographics. At the heart of our hypotheses was the premise that developable personal psychosocial capacities are more significantly linked to resilience than are any immutable demographics.

Methods

Study Design

We conducted secondary analysis of cross‐sectional data from the Practitioner Resilience, Adaptability and Wellbeing Study (PRAWS) (Waddimba et al. 2015a,b), a prospective observational study investigating a community of physicians and mid‐level practitioners of patient care within an integrated delivery system.

Human Subjects Protection

Ethical approval for the study was obtained from the Institutional Review Board at the hosting academic medical center. The project is supervised by an advisory board that is representative of the community from whom participants are drawn.

Study Setting

A health care network comprising a 180‐bed academic medical center, 6 community hospitals, 31 outreach clinics, and 20 school‐based health centers was the setting for our study. It has near monopoly on provision of primary and secondary health care to an underserved population geographically dispersed over a nine‐county region of central New York spanning 5,600 square miles. The medical staff comprises ≥450 physicians and advanced‐practice clinicians employed under a salaried reimbursement model. The study occurred during an institutional freeze on hiring of “nonessential” staff to cut costs, plus the rollout of an EPIC®‐based electronic medical record (EMR). Designated as a level III patient‐centered medical home, the institution was implementing numerous federal‐ and state‐mandated reforms, coinciding with leadership transition at the President/CEO level.

Study Population

We included, in the study sample, health care practitioners on the institutional payroll. We excluded practitioners temporarily hired on locum tenens who lacked firm ties to the organization, plus residents/trainees. Of 493 prospective recruits, 3 left practice during the study's promotion, 5 resigned or were involuntarily separated from the organization, and 12 served on the advisory board or research team and were excluded, leaving a recipient pool of 473 practitioners.

Data Collection

We distributed a multidimensional questionnaire that was piloted among advisory board members and resident physicians, to prospective respondents by postal mail, interoffice mail, and (as a SurveyMonkey® hyperlink) by e‐mail. Completing the five‐page survey took 15–20 minutes on average. In the rollout strategy, we adapted, modified, and deployed standard techniques (Thorpe et al. 2009; Dillman, Smyth, and Christian 2014). The survey was preceded by 2 months of promotion at staff meetings, department forums, grand rounds, and informal gatherings, coupled with an e‐mail campaign via the Medical Staff listserv. The distribution lasted 14 weeks, with additional surveys mailed to nonresponders at the 6‐ and 12‐week points, coupled with further promotional activities. We merged survey data with administrative information provided by the organization about its clinical units.

Variables/Measures

Principal Outcome

Stress resilience was the principal outcome. It was assessed by the Brief Resilience Scale (BRS) (Smith et al. 2008), which consists of three positively worded items (e.g., I tend to bounce back quickly after hard times) and three negatively worded items (e.g., I tend to take a long time to get over set‐backs in my life). Respondents rated agreement with each item on a 5‐point Likert scale ranging from 1 = “strongly disagree” to 5 = “strongly agree.” The BRS is scored by reverse‐coding negative items and then averaging all six items. Most other measures (Smith et al. 2008) assess factors/processes leading to resilience (Ahern et al. 2006) rather than resilience. One review concluded that the BRS was among three most valid measures (Windle, Bennett, and Noyes 2011). It had high internal consistency in our sample (ordinal coefficient alpha [Zumbo, Gadermann, and Zeisser 2007] was 0.93899; p < .0001). We formatted BRS scores both as a continuous variable and a binary outcome of high (scores ≥4)/low resilience (scores ≤2) versus other.

Psychosocial Needs

Autonomy and relational/affiliation needs were assessed with the autonomy and relatedness subscales of the Basic Psychological Needs at Work Scale (Brien et al. 2012). The subscales consist of four positively worded items each (e.g., I can use my judgment when solving work‐related problems for autonomy; When I'm with the people from my work environment, I feel understood for relatedness) with a six‐point Likert‐style response format ranging from 1 (strongly disagree) to 6 (strongly agree). Each subscale is scored by calculating the mean of its items. Respective ordinal coefficients alpha in our sample was 0.9338 for the autonomy subscale and 0.9318 for the relatedness subscale (p < .0001).

Work Meaningfulness

Practitioners' “sense of personal meaning” in patient care practice was assessed by the Personal Meaning in Patient Care scale (Geller et al. 2008), which comprises six items (e.g., Feeling deep connections with my patients). Responses follow a four‐point Likert‐style frequency rating ranging from 1 (not at all) to 4 (a great deal). It is scored by summing up its constituent items. Ordinal coefficient alpha for the scale was 0.9348 in our sample.

Risk Aversion

We utilized two items from the six‐item Risk‐Taking Scale (Pearson et al. 1995) of the Jackson Personality Inventory (Jakcson 1975) to characterize risk attitudes. The items selected were I try to avoid situations that have uncertain outcomes and I rarely, if ever, take risks when there is another alternative. Agreement is rated on a four‐point Likert‐style response format ranging from 1 (strongly agree) to 4 (strongly disagree). We summed the ratings so that higher scores indicated greater risk aversion. The polychoric correlation coefficient between the items was 0.5304 in our sample; respective item‐to‐scale polyserial correlations were 0.9199 and 0.8925.

Intolerance of Ambiguity/Uncertainty

We assessed attitudes toward ambiguity using two items from the 13‐item Stress from Uncertainty subscale of the Physicians' Reactions to Uncertainty in Patient Care Scale (Gerrity, DeVellis, and Earp 1990). The selected items were as follows: The uncertainty of patient care often troubles me and I usually feel anxious when I'm not sure of a diagnosis. Agreement ratings followed a four‐point Likert‐style format ranging from 1 (strongly agree) to 4 (strongly disagree). We summed them such that higher scores indicated greater intolerance for ambiguity. The items had a polychoric correlation of 0.6536, and item‐scale polyserial correlations of 0.9362 and 0.9295, respectively.

Professional Satisfaction

We assessed job satisfaction via a single item whereby respondents estimated the percentage of time that they felt satisfied, not satisfied, and neither satisfied nor dissatisfied, in an adaptation of affect frequency estimates from the Fordyce Emotions Questionnaire (Fordyce 1988). This measure has been adapted to job satisfaction previously (Judge, Boudreau, and Bretz 1994) and its reliability compares with similar well‐being indices (Diener 1984; Fields 2002). As a one‐dimensional construct, overall/global job satisfaction is reliably assessed by single‐item measures (Wanous, Reichers, and Hudy 1997). We formatted satisfaction frequency as both a continuous variable and a binary outcome of satisfied ≥75 percent of the time versus not, and dissatisfaction frequency as both a continuous variable and binary outcome of dissatisfied ≥25 percent versus not.

Workload

We assessed subjective workload quantity by a perceived workload scale specifically designed for the parent study, and described in a previous report (Waddimba et al. 2015a). It comprises five items (e.g., I feel stressed out from having too much work), each rated on a four‐point Likert‐style response format ranging from 1 = “Never or 0 percent of the time” to 4 = “Frequently or >75 percent of the time.” It was scored by summing up the items. Ordinal coefficient alpha for this scale was 0.810 (p < .0001).

Practitioner Demographics

We tested associations between resilience and these individual practitioner demographics: gender, marriage, age, organizational tenure, full‐time/part‐time status, profession, specialty, clinical department, and scope of practice.

Clinical Unit Characteristics

Administrative data obtained from the institution described each clinical unit in terms of its geographic location, number of practitioners, total and per practitioner numbers of administrative managers, nursing staff, other support staff, and number of unfilled vacancies.

Statistical Analysis

The variable selection strategy consisted of generalized linear mixed regressions testing the association between resilience and each factor, as a fixed effect, with clustering by service unit included as a random effect. We also assessed bivariate mixed‐effects logistic regression models of high resilience (BRS ≥4) and low resilience (BRS ≤2). Variables with significant associations in bivariate regressions were entered into (i) a predictive multivariable generalized linear mixed‐effects regression model of BRS scores; (ii) predictive multivariable mixed‐effects logistic regressions of high and (iii) low resilience. Via backward/forward steps, models were reduced by progressively dropping variables that retained no independent significance. Variables were assessed for removal by comparing the goodness of fit, measured by the Akaike Information Criterion and Bayesian Information Criterion, of a model with and without the variable(s). By this systematic process, we identified parsimonious multivariate models that retained only independently significant factors. Analyses were performed in Statistical Analysis Software (SAS) version 9.3 (SAS Inc., Cary, NC, USA).

Results

Of 473 recipients, 308 (65.1 percent) completed the survey. Respondents were 53.8 percent male, 80.9 percent married, 81.9 percent full‐time employees, 59.1 percent doctoral‐level practitioners, and 40.9 percent advanced‐practice clinicians. Mean (95 percent confidence interval) age and organizational tenure were 49.2 (47.9, 50.6) and 10.3 (9.3, 11.3) years. A percentage of 32.8 participants rated themselves as burning out or burned out. A percentage of 41.3 felt satisfied with practice ≥75 percent of the time; 47.1 percent felt dissatisfied ≥25 percent. A percentage of 94.5 respondents gave complete responses on the BRS. The median BRS score was 3.7 (interquartile range = 3.2–4.2). A percentage of 63.9 respondents reported high resilience; 7.6 percent low resilience. Table 1 lists characteristics of the full sample and contrasts highly resilient with low‐resilience peers.

Table 1.

Descriptive Statistics of the Survey Respondents

Variable Overall Study Sample (N = 308) High Resilience (63.9%) Low Resilience (7.56%)
Practitioner demographics
Male gender (%) 53.9 54.8 54.6
Married (%) 80.5 81.0 81.0
Age, years (mean, STD) 49.2 (11.8) 48.4 (12.0) 46.0 (11.2)
Organizational tenure, years (mean, STD) 10.3 (9.0) 9.9 (9.2) 8.8 (8.1)
Physician or other doctor (%) 59.1 54.8 54.6
Advanced‐practice clinician (%) 40.9 34.3 37.25
Works full time (%) 81.8 84.4 90.9
Works in primary health care (%) 32.1 30.1 31.8
Job demands
Perceived workload (median, q1–q3) 12 (10, 14) 11 (9, 13) *** 12.5 (10, 15)
Heavy workload (workload score ≥15, %) 21.8 *** 17.2 * 27.3
Light workload (workload score <10, %) 37.0 *** 44.4 *** 22.7
Psychosocial needs at work
Autonomy (median, q1–q3) 5.3 (4.8, 5.8) 5.5 (4.8, 5.8) *** 4.8 (4.5, 5.3) ***
Relatedness (median, q1–q3) 5.0 (4.3, 5.3) 5.0 (4.5, 5.5) *** 4.5 (4.0, 5.0) ***
Tolerance for uncertainty and risk
Risk aversion (median, q1–q3) 5 (4, 6) 5 (4, 6) 5 (4, 6)
High risk aversion (%) 33.7 *** 32.1 40.9
Low risk aversion (%) 42.8 * 47.3 * 36.4
Intolerance of uncertainty (median, q1–q3) 5 (4, 6) 5 (4, 6) 6 (5, 7)
High intolerance (%) 39.3 *** 29.9 *** 71.4 **
Low intolerance (%) 36.9 *** 44.6 *** 19.1
Meaning/purpose
Perceived meaningfulness (median, q1–q3) 3.3 (2.8, 3.7) 3.3 (2.8, 3.7) 3.3 (2.7, 4.0)
Affective job dis/satisfaction
Percent of time satisfied (median, q1–q3) 60 (30, 80) 75 (50, 85) 30 (20, 50)
Satisfied ≥75 of the time (%) 41.3 52.4 *** 9.1 **
Percent of time dissatisfied (median, q1–q3) 20 (10, 30) 11.1 (5, 25) 40 (25, 55)
Dissatisfied ≥25 of the time (%) 47.1 34.3 *** 72.7 *
Stress resilience
Brief Resilience Scale score (median, q1–q3) 3.7 (3.2, 4.2) 4.0 (3.8, 4.5) 2.2 (2.0, 2.3)
Service unit characteristics
No. of providers on the unit (median, q1–q3) 8 (4, 16) 13 (5, 29) 6 (4, 13)
Small clinical unit (≤5 practitioners, %) 30.7 27.1 50.0 *
Total support staff FTEs (median, q1–q3) 5.1 (2, 9) 5.2 (2, 8) 4.4 (2, 5.5)
Support FTEs per provider (median, q1–q3) 0.3 (0.2, 2.0) 0.3 (0.2, 1.8) 0.4 (0.2, 1.6)
Support staff vacancy FTEs (median, q1–q3) 0.07 (0.0, 0.15) 0.06 (0.0, 0.15) 0.04 (0.0, 0.09)
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Bolded figures indicate statistical significance: *p < .05, **p < .01, ***p < .001.

Table 2 depicts Spearman rank correlations between continuous variables and ordinal scores among high‐ (below the diagonal) versus low‐resilience practitioners (above the diagonal). Appendix SA2 shows results of nonparametric ANOVAs between resilience and the factors under study. In summary, BRS scores were significantly associated with satisfaction frequency ≥75 percent or ≥50 percent; dissatisfaction frequency ≥50 percent or even 25 percent; autonomy and relatedness needs; light or heavy workloads. Relatedness was significantly associated with high, not low, resilience; workload with high, not low, resilience; number of physicians with low but not high resilience; and number of APCs with high but not low resilience. Higher uncertainty intolerance was associated with poorer resilience; less intolerance with greater resilience. Respondents with low risk aversion reported higher resilience. Highly risk‐averse respondents reported less resilience, although this difference was not significant. Correlation between risk aversion and resilience, for the full sample, was negative and marginally nonsignificant (ρ = −0.1155; p = .0510). Meaning scores manifested no significant association with high or low resilience. Neither low nor high meaningfulness was significantly associated with BRS scores. Number of practitioners on a unit was more strongly associated with low than high resilience. Other unit characteristics plus all practitioner demographics had no significant association with resilience. Table 3 outlines results of unadjusted generalized linear mixed‐effects regressions of BRS scores and unadjusted generalized logistic mixed‐effects regressions of high/low resilience.

Table 2.

Spearman Correlations between Resilience and the Continuous/Ordinal Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. Resilience −0.163 0.078 −0.254 0.273 0.154 0.115 −0.504* −0.368 0.158 0.198 0.168 0.183 −0.112 −0.220 −0.499*
2. % time satisfied 0.237** −0.739*** 0.261 0.008 −0.201 −0.381 0.556** 0.065 −0.111 −0.372 −0.109 −0.004 −0.106 −0.158 0.137
3. % time dissatisfied −0.281*** −0.870*** −0.034 0.188 0.178 0.367 −0.441* −0.137 −0.142 0.357 0.138 0.023 0.050 0.468* −0.033
4. Meaningfulness 0.061 0.288*** −0.224** 0.124 0.325 0.522* 0.227 −0.044 0.220 0.086 −0.249 −0.328 0.410 0.404 0.505*
5. Risk aversion −0.062 −0.023 0.057 −0.006 0.529* 0.163 −0.253 −0.572** 0.262 0.219 0.127 −0.058 −0.010 −0.074 −0.235
6. Uncertainty Intolerance −0.220** −0.113 0.138 0.032 0.351*** 0.318 −0.400 −0.555** 0.183 0.089 0.182 −0.070 0.178 0.245 0.086
7. Perceived Workload −0.293*** −0.550*** 0.558*** −0.033 −0.082 0.105 −0.200 −0.035 0.346 0.286 0.117 0.004 0.380 0.369 0.140
8. Relational needs 0.220** 0.390*** −0.395*** 0.160* 0.002 −0.085 −0.263*** 0.646** 0.090 −0.033 −0.333 −0.179 0.111 0.040 0.426
9. Autonomy needs 0.206** 0.393*** −0.403*** 0.206** −0.148* −0.157* −0.208** 0.522*** −0.144 −0.142 −0.383 −0.202 0.147 0.064 0.381
10. Age (years) 0.047 0.008 −0.001 −0.051 −0.309*** −0.126 −0.073 −0.063 −0.127 0.620** −0.012 −0.060 0.219 −0.190 0.128
11. Tenure (years) 0.047 0.057 −0.065 −0.049 −0.098 −0.198** −0.090 0.058 −0.049 0.575*** 0.043 −0.077 0.247 0.150 −0.161
12. No. of unit providers −0.051 0.032 −0.047 −0.144 0.140 0.076 −0.078 −0.155* −0.192* −0.123 −0.074 0.732*** 0.273 0.037 −0.150
13. No. of unit doctors 0.011 −0.080 0.057 −0.143 0.011 0.019 0.121 −0.072 −0.168* 0.031 0.003 0.659*** −0.231 −0.125 −0.101
14. No. of unit APCs −0.116 0.073 −0.053 −0.028 0.131 0.075 −0.096 0.159* −0.143 −0.163* −0.051 0.797*** 0.231** 0.365 0.341
15. Total support staff FTEs −0.125 −0.051 0.082 0.174* 0.017 0.172* 0.164* −0.092 0.012 −0.080 −0.050 0.150* −0.005 0.359*** 0.288
16. Vacant support staff FTEs −0.057 −0.055 0.065 0.055 −0.037 −0.018 0.159* −0.111 −0.153* 0.017 0.026 0.303*** 0.472*** 0.346*** 0.239**
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Spearman correlation coefficients below the diagonal are for providers with high resilience; those above the diagonal are for providers with low resilience; *p < .05; **p < .01; ***p < .001.

Table 3.

Results of Univariate Regressions of Resilience on Each Predictor

Variable Mixed‐Effects Generalized Linear Regressions of BRS Score Mixed‐Effects Logistic Regressions of High Resilience Mixed‐Effects Logistic Regressions of Low Resilience
Beta Coefficient (Standard Error) t‐statistic (p‐value) Beta Coefficient (Standard Error) t‐statistic (p‐value) Beta Coefficient (Standard Error) t‐statistic (p‐value)
Practitioner demographics
Male gender 0.124 (0.094) 1.31 (.191) 0.199 (0.250) 0.80 (.432) 0.260 (0.508) 0.51 (.612)
Married 0.153 (0.114) 1.34 (.181) 0.135 (0.308) 0.44 (.665) 0.101 (0.647) 0.16 (.877)
Age (years) 0.001 (0.004) 0.32 (.748) −0.003 (0.011) −0.29 (.775) −0.021 (0.022) 0.97 (.335)
Tenure (years) 0.001 (0.005) 0.13 (.897) −0.003 (0.014) −0.18 (.859) −0.023 (0.030) −0.79 (.432)
Doctor −0.009 (0.096) −0.09 (.928) −0.456 (0.257) −1.77 (.088) −0.264 (0.525) −0.50 (.620)
Works full time 0.046 (0.123) 0.38 (.706) 0.246 (0.326) 0.76 (.457) 0.906 (0.835) 1.08 (.289)
Works in primary care −0.076 (0.106) −0.72 (.472) −0.121 (0.284) −0.43 (.682) 0.0747 (0.624) 0.12 (.908)
Job demands
Perceived workload quantity −0.077 (0.014) −5.52 (<.0001) −0.158 (0.042) −3.80 (.0002) 0.155 (0.080) 1.94 (.054)
Psychosocial needs at work
Autonomy needs 0.178 (0.042) 4.26 (<.0001) 0.517 (0.131) 3.95 (.0001) −0.431 (0.218) −1.98 (.049)
Relational/affiliation needs 0.197 (0.043) 4.61 (<.0001) 0.502 (0.130) 3.86 (.0002) −0.372 (0.231) −1.61 (.108)
Tolerance for uncertainty/risk
Uncertainty intolerance −0.204 (0.031) −6.67 (<.0001) −0.466 (0.101) −4.60 (<.0001) 0.705 (0.224) 3.14 (.002)
Risk aversion −0.060 (0.034) −1.78 (.076) −0.143 (0.092) −1.56 (.121) 0.168 (0.179) 0.94 (.348)
Meaning/purpose
Perceived meaningfulness 0.071 (0.075) 0.94 (.346) 0.172 (0.203) 0.85 (.398) 0.0127 (0.407) 0.03 (.975)
Affective job dis/satisfaction
% time satisfied with practice 0.011 (0.002) 6.90 (<.0001) 0.029 (0.005) 5.69 (<.0001) −0.020 (0.010) −2.09 (.038)
Satisfied ≥75% of the time 0.581 (0.087) 6.68 (<.0001) 1.583 (0.299) 5.29 (<.0001) −2.127 (0.789) −2.70 (.011)
% of time dissatisfied −0.013 (0.002) −6.14 (<.0001) −0.034 (0.007) −5.22 (<.0001) 0.017 (0.011) 1.59 (.112)
Dissatisfied ≥25% of time −0.550 (0.088) −6.27 (<.0001) −1.387 (0.267) −5.19 (<.0001) 1.121 (0.546) 2.05 (.048)
Service unit characteristics
No. of providers on the unit 0.008 (0.004) 2.02 (.044) 0.025 (0.011) 2.27 (.026) −0.066 (0.035) −1.87 (.065)
No. of physicians on the unit 0.008 (0.007) 1.09 (.276) 0.022 (0.021) 1.07 (.286) −0.094 (0.059) −1.58 (.118)
No. of APCs on the unit 0.011 (0.006) 2.00 (.047) 0.039 (0.017) 2.31 (.024) −0.064 (0.052) −1.22 (.228)
Total support staff FTEs 0.00003 (0.006) 0.01 (.996) 0.001 (0.016) 0.08 (.939) −0.038 (0.041) −0.92 (.360)
Support staff vacancy FTEs −0.099 (0.263) −0.38 (.707) −0.246 (0.713) −0.35 (.731) −0.095 (1.507) −0.06 (.950)
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Bolded figures indicate statistical significance at the α = 0.05 level.

Table 4 illustrates results of multivariable models. In the most parsimonious generalized linear mixed‐effects model, these variables were significantly associated with resilience, across its entire spectrum, independent of other covariates: relational needs, uncertainty intolerance, workload, satisfaction frequency, and number of unit practitioners. The more gratified a practitioner's relational needs were, the more resilience he or she reported. The more a practitioner tolerated uncertainty, the more resilient he or she was. Practitioners with heavier workloads reported less resilience. More frequent satisfaction was associated with greater resilience. Units with more practitioners seemed to be more resilient workplaces. Once these key factors were accounted for, practitioner demographics and most unit characteristics had no significant effect.

Table 4.

Parsimonious Mixed‐Effects Multivariable Models of Resilience

Model Mixed‐Effects Multivariate Generalized Linear Regression Model of Resilience Mixed‐Effects Multivariate Logistic Regression Model of High Resilience Mixed‐Effects Multivariate Logistic Regression Model of Low Resilience
Parameter Beta Coefficient (Standard Error) 95% CI of Beta Coefficient F‐test (p‐value) Beta Coefficient (Standard Error) Odds Ratio (95% CI) F‐test (p‐value) Beta Coefficient (Standard Error) Odds Ratio (95% CI) F‐test (p‐value)
Intercept 4.069 (0.340) 3.392–4.747 n/a 0.191 (0.920) n/a n/a −6.385 (1.580) n/a n/a
Intolerance of ambiguity/uncertainty −0.156 (0.030) −0.217 to −0.095 25.65 (<.0001) −0.435 (0.114) 0.648 (0.517–0.811) 14.43 (.0002) 0.675 (0.240) 1.963 (1.224–3.149) 7.92 (.005)
Satisfied with practice ≥75% of time 0.352 (0.095) 0.164–0.540 13.64 (.0003) 1.291 (0.325) 1.030 (1.004–1.056) 15.82 (<.0001) −1.960 (0.824) 0.141 (0.026–0.751) 5.65 (.023)
Number of providers on the unit 0.007 (0.003) −0.000 to 0.013 3.65 (.058) 0.029 (0.013) 3.635 (1.917–6.893) 5.32 (.022)
Perceived workload quantity −0.031 (0.014) −0.059 to −0.002 4.50 (.035)
(Gratified) Needs for relatedness 0.111 (0.043) 0.027–0.196 2.60 (.010)
(Gratified) Needs for autonomy 0.362 (0.139) 1.436 (1.093–1.887) 6.82 (.010)
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Each of these was the most parsimonious multivariable model for its outcome, after dropping covariates that retained no independent statistical significance.

In the generalized logistic mixed‐effects models, factors with a significant, independent association with high resilience were lower uncertainty intolerance, more frequent satisfaction, and number of practitioners on a unit. Low resilience was most significantly associated with less frequent satisfaction and greater intolerance of uncertainty. A unit increase in autonomy needs gratification was associated with a 1.4‐fold (CI = 1.1–1.9) average increase in odds of high resilience, but it was not independently associated with odds of low resilience. A unit increase in uncertainty intolerance was associated with an average decrease of 0.6 (CI = 0.5–0.8) in odds of high resilience and an increase of 1.96 (CI = 1.22–3.15) in odds of low resilience. Being satisfied ≥75 percent of the time was associated with an increase by 1.0 (CI = 1.0–1.1) in the average odds of high resilience; and a decrease by 0.1 (CI = 0.0–0.8) in odds of low resilience. An additional practitioner on a unit was associated with an average increase by 3.6 (CI = 1.9–6.9) in odds of high resilience, but it was not independently associated with low resilience.

Discussion

In this study, we investigated the factors significantly associated with resilience among doctors and mid‐level practitioners employed by a group‐model health care network in a rural, underserved region of upstate New York. Across its entire spectrum, resilience was most associated with how much each practitioner's relational needs were met, how well they tolerated uncertainty, how much their work was, how frequently they were satisfied, and how many practitioners their unit had. Practitioner demographics and other clinical‐unit characteristics had no independent effect. In multivariable logistic models, odds of high resilience were most strongly associated with greater tolerance of uncertainty, more frequent satisfaction, and working on a unit with more practitioners; low‐resilience odds were associated with greater intolerance of uncertainty, and less frequent satisfaction.

Among the factors significantly associated with resilience was the proportion of time that a practitioner was satisfied. Our findings suggest that more frequent happiness at work builds/sustains resilience, whereas infrequent happiness undermines resilience. Higher practitioner satisfaction enhances patient satisfaction and outcomes (Haas et al. 2000; Grembowski et al. 2005; DeVoe et al. 2007; Williams et al. 2007a). Satisfaction lowers job‐related stress/anxiety, burnout, and attrition (Doan‐Wiggins et al. 1995; Buchbinder et al. 2001; Linzer et al. 2001; Pathman et al. 2002; Landon et al. 2006; Keeton et al. 2007). Practitioner satisfaction is increasingly recognized as a marker of institutional performance (Wallace, Lemaire, and Ghali 2009; Spinelli 2013; Bodenheimer and Sinsky 2014). Subject to confirmation by longitudinal studies or quasi‐experiments, our multivariable models suggest a dual effect for affective satisfaction; that is, frequent satisfaction buffers resilience while frequent dissatisfaction erodes resilience. Positive emotional/cognitive habits can be learned (Seligman 2006). Practitioners could thus be trained in healthy responses to stress that build and sustain resilience (Askin 2008).

Resilience was independently associated with needs for relatedness. Supportive professional relationships help practitioners to cope with work stress (Myers 2001), reducing likelihood of burnout (Hoff, Whitcomb, and Nelson 2002). Social support from peers enhances well‐being more than employee assistance programs (Hu et al. 2012). Among employed practitioners, positive peer relationships influence quality of work life more powerfully than staff support, job control, income, or time pressure (Karsh, Beasley, and Brown 2010). Quality of work relationships is linked to attrition from practice (Hoff, Whitcomb, and Nelson 2002; Masselink, Lee, and Konrad 2008). Individuals with positive feelings about their job build stronger professional relationships (Fredrickson and Losada 2005). Thus, happiness at work enhances resilience both directly and indirectly, by improving relations with peers (Jensen et al. 2008). Health care organizations can promote resilience by fostering social networks whereby practitioners facing adversity can interact (Gittell 2008). Our measures did not differentiate needs for individual versus role relationships, a matter that further research can address. Future studies must investigate social ecologies of resilience and test evidence‐based resilience‐promotion strategies based on social capital (Ungar 2012). Our study happened in the shadow of a new, network‐wide EMR system that increased isolated work time with a computer. Personal contact with patients is a key ingredient in practitioners' work lives (Fairhurst and May 2006), yet EMRs often reduce communication with patients (Hill, Sears, and Melanson 2013). The isolating effect of EMRs (Babbott et al. 2014) and ways of addressing its impact on resilience merit further study. Our finding, of relational needs being more significant in models of the whole spectrum of resilience than in models of its high or low extremes, needs further confirmation and clarification by future studies.

In multivariable logistic models, higher intolerance of uncertainty was associated with low resilience, and better tolerance with high resilience. Doctors' intolerance for uncertainty is linked to more expensive clinical decisions and poorer practice performance (Geller et al. 1993; Allison et al. 1998; Kvale et al. 1999; Benbassat, Pilpel, and Schor 2001; Ghosh 2004; Carney et al. 2007; Wayne et al. 2011), plus reduced job satisfaction (Bovier and Perneger 2007) and increased distress (Benbassat et al. 2011). Cognitive inflexibility associated with uncertainty intolerance hampers adaptability to stressful change (Bonanno et al. 2004). Our finding that ambiguity tolerance was significantly associated with resilience reinforces the findings of Cooke and colleagues (Cooke, Doust, and Steele 2013). This argues for incorporation of ambiguity tolerance training/coaching into medical education (Luther and Crandall 2011).

The significance of perceived workload, in the generalized linear model, supports reports that link higher workloads to increased job distress among physicians (Williams et al. 2007a). Workload perceptions are a more powerful predictor of well‐being than objective measures of workload (Mainous, Ramsbottom‐Lucier, and Rich 1994; Shirom, Nirel, and Vinokur 2010). Improvement in the workplace's relational climate (Mohr, Benzer, and Young 2013) plus enhancements in the control/autonomy enjoyed by the practitioner and the level of organizational support (Shirom, Nirel, and Vinokur 2006; Ilies, Dimotakis, and De Pater 2010) might buffer the resilience‐sapping effect of heavy workload on clinicians.

In our study, autonomy needs were independently associated with odds of high, but not low resilience despite some interaction with relatedness. This could be due to the specific context of our study. Medical professionals in our system surrendered logistical/administrative autonomy by entering salaried employment, exchanging independent practice for the benefits of a large organized group environment with residual clinical/knowledge autonomy. Due to reduced administrative autonomy, employed practitioners lean more on social support from peers (Fernet, Gagné, and Austin 2010). Nevertheless, highly resilient practitioners were those whose expectations for autonomy were being gratified. Health care organizations that adopt a group‐employment model might be better served by recruiting practitioners with less‐than‐average needs for individual autonomy, or who value relatedness more than autonomy.

Unlike Cooke, Doust, and Steele (2013), who found the personal meaning in patient care to be associated with resilience among Australian primary care practitioners, we detected no significant bivariate or multivariate effect for meaningfulness of patient care on resilience. This might be due to confounding, in our sample, between work meaningfulness and variables such as the proportion of time practitioners were satisfied, autonomy needs, relational needs, and unit staffing levels (see Table 2). One study among 220 certified nurse midwives found that meaningfulness predicted reduced burnout but was not significantly associated with the construct of resilience (Caza 2007). After controlling for whether practitioners considered work a job, profession, or calling, those with a calling orientation were less resilient (Caza 2007). Some suggest that finding work too meaningful has “unhealthy” consequences (Cardador and Caza 2012) such as reduced perseverance and resilience. Future studies should investigate this further and examine whether an optimum threshold of meaningfulness exists.

Our finding that demographics are not strongly associated with resilience is very instructive. Personal psychosocial capacities, which can be developed by training/coaching, should thus be the main focus of efforts at promoting resilience. Future studies should evaluate plausible resilience‐enhancing programs and shed light on actionable interventions at the individual, work group/unit, and organizational level.

Strengths and Limitations

The survey methodology could have introduced selection biases. We found no significant demographic differences between respondents and nonresponders, except for organizational tenure (median of 7.8 years for respondents vs. 4.8 for nonresponders; p < .0001). The cross‐sectional observation study design makes it hard to establish which factors precede others. The rural study setting limits the external validity of our findings. The findings need to be confirmed in longitudinal or quasi‐experimental studies across multiple, diverse settings. The intercept (constant) in multivariable models (see Table 4) had a significant beta coefficient, indicating that resilience is likely influenced by additional variables that we did not measure. Nevertheless, we assessed many important constructs using valid and reliable scales from the literature. We linked survey data with administrative data, such that our analysis was not restricted to self‐report variables but included objective measures. Our multipronged analysis strategy was robust to clustering (practitioners nested within service units) in the sample. We sampled practitioners from diverse disciplines, clinical departments, medical specialties, and geographic locations. Our response rate (65.1 percent) compares favorably with other studies among medical professionals, a further strength.

Implications

Resilience was not associated with immutable demographics. It was most significantly associated with factors such as practice satisfaction, relational needs, ambiguity tolerance, workload, and practitioner supply. These factors can be tracked to monitor the quality of work life for employed clinicians. Continued medical education can re/train practitioners to tolerate clinical ambiguity and maintain a positive affect during adversity. Managers of health institutions should facilitate building of supportive professional networks/communities that increase social capital and reduce loneliness/isolation, particularly in rural settings. Practice organizations should redesign systems to enhance interprofessional, multidisciplinary teamwork to alleviate perceived inequity in division of clinical labor and increase gratification of relational needs. Practitioners must increase their awareness of facilitators of resilience and the importance of a positive, optimistic, and courageous demeanor.

Conclusion

Across the resilience spectrum, practitioners' capacity was most strongly associated with their uncertainty tolerance, how often they felt satisfied with work, their workload, the number of practitioners on their unit, and the extent to which their relational needs were met. Practitioners at the high end of the resilience spectrum were more frequently satisfied, were better at tolerating uncertainty, had more of their autonomy needs gratified, and typically served on a unit with more practitioners. The least resilient ones were more intolerant of uncertainty and less frequently satisfied with their practice. The number of practitioners on a unit and fulfillment of autonomy needs were associated with high, but not low, resilience. We detected no significant effects for practitioner demographics; perceived meaningfulness of clinical practice; or for most of the practice unit characteristics.

Supporting information

Appendix SA1: Author Matrix.

Click here for additional data file. (1.1MB, pdf)

Appendix SA2: Unadjusted Associations of Resilience Outcomes with Practitioner‐ and Unit‐Level Variables.

Click here for additional data file. (15.5KB, pdf)

Acknowledgments

Joint Acknowledgment/Disclosure Statement: The authors are very grateful to all physicians, dentists, pharmacists, and advanced‐practice clinicians who generously responded to the PRAWS baseline survey. We thank our Advisory Board for their wise stewardship and guidance. Reginald Knight, MD, the president of the Bassett Medical Staff Group, and his executive were quite helpful in championing and publicizing the survey. Shannon Crisman coordinated the early stages of the PRAWS project; Cosby Gibson provided research assistance. Earlier drafts of this manuscript benefited from critical comments by Richard Brown, MD, an attending psychiatrist at Bassett Medical Center in Cooperstown, NY, and David Mohr, PhD, a research scientist at the Veterans Affairs Center for Healthcare Outcomes and Implementation Research (CHOIR) in Boston, MA. We thank the editors and peer reviewers of the Health Services Research journal for their numerous excellent suggestions. Laura Dixon, MLS, the manager of McKenzie Medical Library at Bassett Medical Center and her staff, procured, on behalf of the Research Team, the copyright permissions and manuals for the proprietary scales that are utilized in the PRAWS project. No extramural funding supported this study. The authors' employment with the Research Institute at Bassett Healthcare Network is partially supported by the Stephen Carlton Clark Research Endowment Fund.

Disclosures: None.

Disclaimers: None.

Notes

1

“The development of a disease or morbid condition. Also called nosogenesis”; The American Heritage Medical Dictionary, © Houghton Mifflin Company, 2007.

2

“The process of healing, recovery and repair. The term was first used by Aaron Antonovsky to contrast with pathogenesis”; Mosby's Dictionary of Complementary and Alternative Medicine, © Elsevier Publications, 2005.

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Associated Data

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Supplementary Materials

Appendix SA1: Author Matrix.

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Appendix SA2: Unadjusted Associations of Resilience Outcomes with Practitioner‐ and Unit‐Level Variables.

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