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Moral Resilience Reduces Levels of Quiet Quitting, Job Burnout, and Turnover Intention among Nurses: Evidence in the Post COVID-19 Era

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17 December 2023

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18 December 2023

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Abstract
The aim of the study was to examine the impact of moral resilience on quiet quitting, job burnout, and turnover intention among nurses. A cross-sectional study was implemented in Greece in November 2023. The revised Rushton Moral Resilience Scale was used to measure moral resilience among nurses, the Quiet Quitting Scale to measure levels of quiet quitting, and the single item burnout measure to measure job burnout. Moreover, a valid six-point Likert scale was used to measure turnover intention. All multivariable models were adjusted for the following confounders: gender, age, understaffed department, shift work, and work experience. The multivariable analysis identified a negative relationship between moral resilience and quiet quitting, job burnout, and turnover intention. In particular, we found that increased response to moral adversity and increased moral efficacy were associated with decreased detachment score, lack of initiative score, and lack of motivation score. Additionally, personal integrity was associated with reduced detachment score, while relational integrity was associated with reduced detachment score, and lack of initiative score. Moreover, response to moral adversity was associated with reduced job burnout. Also, increased levels of response to moral adversity were associated with lower probability of turnover intention. Moral resilience can be an essential protective factor against high levels of quiet quitting, job burnout, and turnover intention among nurses.
Keywords: 
Subject: Public Health and Healthcare  -   Nursing

1. Introduction

Nurses, as frontline healthcare professionals, are the most important resource of health organizations’ and it is through their contribution that the organizations’ can achieve their goals. These include meeting the health needs of an increasingly ageing population, dealing with complex cases due to the increased number of patients with comorbidities and reducing errors and adverse events that cause harm to a significant number of hospitalized patients [1,2]. However, health organizations face significant organizational and operational weaknesses, which include understaffing, inadequate resources, poor collaboration and insufficient leadership support [3]. These make nurses' work challenging, negatively affecting their workplace well-being [4,5], and ultimately leading to failure to achieve organizational goals [6,7]. The COVID-19 pandemic found health organizations struggling with the same organizational problems, further burdening nurses [8], who now had to deal with extremely high workloads as well as significant moral issues when providing care to patients with COVID-19 [9].
One of the longstanding issues of nurses' well-being, with significant consequences for both nurses and health care organizations, is burnout. It is estimated that burnout internationally affects one in three nurses [10]. Organizational characteristics that have been identified as risk factors for burnout include poor leadership with inadequate support for nurses, understaffing, lack of resources, decreased nurses' participation in hospital affairs, as well as workload and working longer hours [11,12,13]. The COVID-19 pandemic has placed organizations under extreme pressure, with large number of patients requiring emergency and intensive care. Also, during this period, the organizational weaknesses of the healthcare care organizations continued to emerge once again [14]. The combination of extremely difficult working conditions and organizational deficiencies increased the percentage of nurses with burnout to 60% [15]. Nurses who experience high levels of burnout have a compromised quality of life and, although they may still be in the early years of their careers, they are at risk of developing cognitive dysfunction, depression, and impaired sleep [16,17]. On the other hand, insomnia and depression in nurses is associated with the onset of burnout [18]. Consequently, many nurses who experience well-being problems often find themselves trapped in a vicious cycle where one factor feeds into another, ultimately leading nurses to a perpetual mental distress and reduced performance [18,19]. Patients hospitalized in nursing units where nurses experience high levels of burnout, report dissatisfaction with care and are at high risk of experiencing an adverse event such as medication errors, infections and falls [19].
Nurses, in an effort to ensure a healthy working environment, they choose to move to another organization, or eventually to resign and change career orientation. When nurses' work environment is characterized by reduced opportunities for promotion, long hours per week with several night shifts, frequent incidents of violence, and high rates of stress and burnout, nurses manifest their turnover intention [20]. When the managements of healthcare organizations recognize the contribution of nurses and work to ensure their well-being, what we call organizational support, then nurses’ work engagement, affective commitment and satisfaction are increased and therefore a reduction in turnover intention is achieved [20,21,22,23]. Low patient satisfaction, increased likelihood of errors and high administrative costs associated with nurse’s turnover [24]. Although turnover intention refers to the likelihood of a nurse leaving an organisation in the near future, however, studies have shown that a significant proportion of nurses ultimately decide to turnover [25]. Therefore, high turnover intention rates are a warning sign that should motivate health service administrations to work towards improving the working environment of nurses.
The COVID-19 pandemic created new working conditions, as all sectors of business were affected by the protection measures and many vacancies were lost. It was therefore very difficult for workers, including nurses, to give up their jobs and find a new one. So, they decided to stay in their jobs but opt for quiet quitting. By choosing quiet quitting, the employee significantly reduces his or her performance by performing completely necessary duties, not expressing new opinions or making suggestions, and avoiding coming in early or staying overtime. She/he basically meets the minimum requirements of his job in order not to be fired. [26,27]. In the United States it is estimated that 50% of business employees have opted for quiet quitting [28]. As it is now difficult for workers to change their working environment, they choose quiet quitting as a form of self-protection from the highly demanding working conditions, and at the same time to achieve a balance between their family commitments and work. Early studies in the health sector show that health professionals also opt for quiet quitting, with nurses showing the highest rates [29]. Nurses who are exhausted are more likely to choose quiet quitting, while job satisfaction plays a mediating role between burnout and quiet quitting [30]. Although quiet quitting is chosen as a reaction to difficult working conditions, it nevertheless seems to be a temporary solution, and ultimately the nurses who choose quiet quitting are those who have higher turnover intention [31].
In their daily practice nurses are faced with significant challenges that affect them and the quality of care. One of them is moral distress, where nurses are aware of their moral obligation and have decided to act based on their moral principles, but some factors prevent them from implementing their decisions [32]. The factors leading to moral distress can be categorized into three types, clinical situations, internal constraints and external constraints. The first two types relate mainly to nurses' lack of knowledge and skills towards the daily situations they have to cope with, while the third relates to organizational and operational dysfunctions [33]. Regardless of its source, moral distress is associated with a negative impact on nurses' mental health, burnout, turnover intention and quality of care [34,35,36]. An essential skill for nurses in the management of moral distress is moral resilience, which is defined as “the capacity of an individual to sustain or restore their integrity in response to moral complexity, confusion, distress, or setbacks” [37]. Moral resilience can be cultivated through mindfulness meditation, ethics education and improved organizational support [38]. The higher the moral resilience, the higher the nurses' engagement with the healthcare organization and the lower the level of burnout and turnover intention [39,40].
To the best of our knowledge, this is the first study that investigated the impact of moral resilience on levels of quiet quitting among nurses. Moreover, we examined the impact of moral resilience on job burnout and turnover intention.

2. Materials and Methods

2.1. Study Design

We conducted a cross-sectional study in Greece. Data collection was performed during November 2023. Nurses who have been working in healthcare services for at least two years and those who understood the Greek language were eligible to participate in our study. Healthcare services included hospitals, health centers, and nursing homes.
We obtained a convenience sample in our study. First, we created an anonymous online version of the study questionnaire using Google forms and then we disseminated it through Facebook, Instagram, Viber, and WhatsApp. Regarding Facebook, we also posted the questionnaire on nursing groups. Second, we sent the questionnaire to nurses using our e-mails contacts. Finally, we conducted paper-and-pencil interviews with nurses that they want to participate in our study without using the online questionnaire.
Considering a low effect size (f2=0.02) of moral resilience on quiet quitting, job burnout, and turnover intention, the number of independent variables (four predictors and five confounders), a confidence level of 99%, and a margin of error of 5%, sample size was estimated at 921 nurses.

2.2. Measures

We used the revised Rushton Moral Resilience Scale (RMRS) to measure moral resilience among nurses [41]. The revised RMRS includes 16 items and demonstrates a four-factor structure: responses to moral adversity, personal integrity, relational integrity, and moral efficacy. Each factor includes four items. Score on each factor ranges from 1 to 4. Higher values indicate higher levels of moral resilience. The RMRS-16 has been validated in Greek language [42]. In our study, Cronbach’s alpha for the factors “responses to moral adversity”, “personal integrity”, “relational integrity”, and “moral efficacy” were 0.665, 0.782, 0.783, and 0.765 respectively.
We used the Quiet Quitting Scale (QQS) to measure levels of quiet quitting among nurses [43]. The QQS has a three-factor structure: detachment (four items), lack of initiative (three items), and lack of motivation (two items). Score on each factor ranges from 1 to 5. Higher values indicate higher levels of quiet quitting. We used a suggested cut-off score for the QQS to divide nurses in quiet quitters and non-quiet quitters [44]. In our study, Cronbach’s alpha for the factor’s detachment, lack of initiative, and lack of motivation were 0.722, 0.714, and 0.747 respectively.
We used the single item burnout measure to measure job burnout [45]. In particular, we used the valid Greek version of the single item burnout measure [46]. This item takes values from 0 (not at all burnt out) to 10 (extremely burnt out).
We used a six-point Likert scale to measure turnover intention among nurses [47]. In that case, we asked nurses “How often have you seriously considered leaving your current job?” and answers were never (1), rarely (2), sometimes (3), somewhat often (4), quite often (5), and extremely often (6). According to the developers of the scale, values ≤ 3 denote low level of turnover intention, while values ≥ 4 denote high level of turnover intention.
Moreover, we considered gender (females or males), age (continuous variable), understaffed department (no or yes), shift work (no or yes), and work experience (continuous variable) as potential confounders in the relationship between moral resilience and quiet quitting, job burnout, and turnover intention.

2.3. Ethical Issues

The Ethics Committee of the Faculty of Nursing, National and Kapodistrian University of Athens approved our study protocol (approval number; 474, November 2023). We conducted our study in an anonymous and voluntary basis. We obtained informed consent from nurses. Additionally, we conducted our study in accordance with the Declaration of Helsinki [48].

2.4. Statistical Analysis

We use numbers and percentages to present categorical variables. Moreover, we use mean, standard deviation, median, minimum value, maximum value, skewness value, and kyrtosis value to present continuous variables. We employed Kolmogorov-Smirnov test and Q-Q plots to assess the distribution of continuous variables. We found that scores on moral resilience, quiet quitting, and job burnout followed normal distribution. Thus, we used linear regression analysis to identify the impact of moral resilience on quiet quitting and job burnout. Since the turnover intention was a dichotomous variable (low versus high level of turnover intention), we used logistic regression analysis to examine the impact of moral resilience on turnover intention. First, we performed univariate regression analysis with quiet quitting, job burnout, and turnover intention as the dependent variables. Then, we constructed multivariable linear and logistic regression models to assess the net impact of moral resilience by eliminating the following confounders: gender, age, understaffed department, shift work, and work experience. In case of linear regression analysis, we present coefficient betas, 95% confidence intervals, p-values, and R2. In case of logistic regression analysis, we present odds ratios, 95% confidence intervals, p-values, and R2. We considered results as statistically significant when p < 0.05. IBM SPSS 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.) was used for statistical analysis.

3. Results

3.1. Demographic Characteristics

Study population included 957 nurses. Most of nurses were females (88.2%) and shift workers (76.3%). Moreover, 78.5% nurses reported that they work in understaffed departments. Mean age was 36 years, while mean number of work experience was 10.9 years.
Table 1. Demographic characteristics of nurses (N=957).
Table 1. Demographic characteristics of nurses (N=957).
Characteristics N %
Gender
Females 844 88.2
Males 113 11.8
Agea 36.0 10.3
Understaffed department
No 206 21.5
Yes 751 78.5
Shift work
No 227 23.7
Yes 730 76.3
Years of work experiencea 10.9 9.9
a mean, standard deviation

3.2. Study Scales

Descriptive statistics for the study scales are shown in Table 2. Mean moral resilience score was 2.87 indicating moderate levels of moral resilience in our sample. We found higher levels of resilience regarding personal integrity (mean = 3.41) and moral efficacy (mean = 3.05), and lower levels of resilience regarding relational integrity (mean = 2.71) and response to moral adversity (mean = 2.29).
Mean quiet quitting score was 2.43. Using the cut-off point for the quiet quitting scale, 71.9% (n=688) of nurses were considered as quiet quitters, while 28.1% (n=269) were considered as non-quiet quitters. Levels of lack of motivation (mean = 2.91) was higher than levels of lack of initiative (mean = 2.40) and levels of detachment (mean = 2.20) in our sample.
Mean job burnout score was 7.29 indicating high level of burnout among nurses. Among our sample, 51.8% (n=496) reported high level of turnover intention with values on turnover intention scale ≥ 4, while 48.2% (n=461) reported low level of turnover intention with values on turnover intention scale ≤ 3.

3.3. Impact of Moral Resilience on Quiet Quitting, Job Burnout, and Turnover Intention

We found a negative relationship between moral resilience and quiet quitting. In particular, our multivariable linear regression analysis identified that increased response to moral adversity and increased moral efficacy were associated with decreased detachment score, lack of initiative score, and lack of motivation score. Moreover, personal integrity was associated with reduced detachment score, while relational integrity was associated with reduced detachment score, and lack of initiative score. Table 3 shows linear regression models with quiet quitting as the dependent variable.
Additionally, multivariable linear regression analysis revealed a negative relationship between response to moral adversity and job burnout (Table 4). We did not find statistically significant relationships between personal integrity, relational integrity, and moral efficacy and job burnout (Table 4).
Similarly, according to our multivariable logistic regression model, increased levels of response to moral adversity were associated with lower probability of turnover intention. The three other aspects of moral resilience (personal integrity, relational integrity, and moral efficacy) did not have an impact on turnover intention. We present logistic regression analysis with turnover intention as the dependent variable in Table 5.

4. Discussion

The present study included nurses and highlighted their moderate level of moral resilience, the high percentage of them choosing quiet quitting, the high degree of burnout they experience and the high percentage reporting turnover intention. It was also found that high levels of nurses’ moral resilience reduces the likelihood of choosing quiet quitting, reduces burnout levels and negatively affects turnover intention.
Quiet quitting is a modern phenomenon that emerged in the COVID-19 era and its study is still limited in the health sector. The present study is the first to explore the relationship between moral resilience and quiet quitting. As nurses over time work under difficult conditions with limited organizational support, they find that these difficult conditions are worsening and the management of health care organizations are failing to improve them. Nurses have therefore decided to take action against this situation, they choose quiet quitting by reducing their efforts, not to let this burden affect them and to achieve a work-life balance. Reducing effort, however, can have consequences for patient care, which is already often plagued by a significant number of adverse events, errors and missed care [49,50]. The effort of the administrations of health care organizations to achieve better outcomes must undoubtedly be based on the effectiveness of the nursing staff, who often work under moral distress situations. Although the relationship between moral resilience and quiet quitting behavior has not yet been investigated, there are studies showing the negative effect of moral resilience on the three subscales of quiet quitting (detachment, lack of initiative, lack of motivation) [51,52,53]. Moral resilience can be cultivated both through training or coach role programs and through the ongoing support of supervisors through specific strategies [54,55,56]. Also, at the organizational level, the interventions which include communication programs, strengthening physician–nurse collaboration, nursing involvement in clinical decision-making and end-of-life issues, social support, using a resiliency bundle, interdisciplinary discussion, and promoting nurses’ ethical and communication skills can enhance the moral resilience of nurses [57].
Although we are in the post COVID-19 era, high levels of nurses’ burnout continue to be recorded. This can be explained by the fact that during the COVID-19 pandemic nurses worked in extremely difficult situations and the high levels of burnout continue to affect them [8,58]. In addition to this, situations of moral distress also affected nurses. Limited access to personal protective equipment, fear of transmitting the infection to their family members, caring for patients without family members present, and caring for patients dying without family or clergy present, made up the main moral distress factors that led nurses to withdrawn, burnout and turnover intention [59,60]. Nurses often find themselves trapped in a vicious cycle where burnout leads to moral distress and then moral distress fuels burnout [59]. Through enhancing nurses' moral resilience, the reduction of moral distress and burnout can be achieved [59,61].
The high percentages of nurses who declare their turnover intention should motivate health care administrations, as a significant percentage of nurses who declare turnover intention eventually resign from the profession [25]. Although nurses choose quiet quitting as an alternative to protect them from difficult working conditions, this practice is not enough to keep nurses in their jobs either. Study showed that nurses who choose quiet quitting are more likely to declare turnover intention [31]. As estimates show that by 2030 there will be 4.5 million nursing staff shortages worldwide, the nurses' turnover phenomenon will further exacerbate this situation [62]. Organizational support is a protective factor against nurses' turnover intention [63]. Also, the moral resilience can be an effective tool to halt nurses from leaving the profession, starting even from the youngest nurses, in order to shield them in a timely manner against the difficult working environment they will be facing [61,64].
Our study had several limitations. We used a convenience sample of nurses in Greece and although we achieved the minimum sample size requirements, further studies with representative and random samples would add valuable information. Moreover, studies with nurses in other countries and different clinical settings can expand knowledge. Additionally, we cannot infer a causal relationship between moral resilience and quiet quitting, job burnout, and turnover intention since we performed a cross-sectional study. We used a single item to measure turnover intention and although this tool is valid, scholars can also use other valid instruments to measure turnover intention. Moreover, we used self-reported questionnaires to measure our variables and thus information bias is probable in our study. Actual measurement of turnover among nurses can reduce this bias. We eliminated confounding from several demographic and job variables but several other variables can confound the relationship between moral resilience and quiet quitting, job burnout, and turnover intention, such as managers’ attitudes, personality characteristics, and wage. Our study investigated for first time the impact of moral resilience on quiet quitting in a sample of nurses. Therefore, we should generalize our results with care. Further studies with less bias and different samples should be conducted to improve knowledge.

5. Conclusions

Nurses are at the cutting edge of providing quality and safe health care. Although we are in the post COVID-19 era and hospitals have returned to normal operations, nursing staff continue to face significant well-being issues and are ready to leave the profession. Quiet quitting can compromise quality of care, burnout has already been linked to errors and turnover intention is a precursor to nurses withdrawing, undermining any effort to ensure adequate staffing. Even the administrations of health care organizations are unable to improve the working environment of nurses, moral resilience can be the intervention that reduces these consequences. Through educational programs, moral resilience can be cultivated and the nurses can promote and maintain a high level of resilience. At the same time organizational interventions can create that work environment that will reduce moral distress and promote moral resilience [65]. All these can reduce nurses’ likelihood of developing burnout, manifest quiet quitting and declare turnover intention.

Author Contributions

Conceptualization, P.G.; methodology, P.G., I.M., D.K.; software, P.G., O.K.; validation, A.K., I.V., O.S., O.K.; formal analysis, P.G., A.K., and I.V.; investigation, P.G., A.K., I.V., O.S., O.K.; resources, P.G., A.K., I.V., O.S., O.K.; data curation, I.M., A.K., I.V., O.S., O.K.; writing—original draft preparation, P.G., I.M., A.K.; writing—review and editing, P.G. I.M., A.K., I.V., O.S., O.K., and D.K.; supervision, P.G.; project administration, P.G. and D.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by The Ethics Committee of the Faculty of Nursing, National and Kapodistrian University of Athens (approval number; 474, November 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We acknowledge all the participants who make this study possible.

Conflicts of Interest

The authors declare no conflicts of interest.

Public Involvement Statement

No public involvement in any aspect of this research.

Guidelines and Standards Statement

This manuscript was drafted against the (STROBE) for a cross-sectional study, descriptive research.

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Table 2. Descriptive statistics for the study scales (N=957).
Table 2. Descriptive statistics for the study scales (N=957).
Scale Mean Standard deviation Median Minimum value Maximum value Skewness Kyrtosis
Moral resilience 2.87 0.39 2.88 1.31 4.00 0.03 0.26
 Response to moral adversity 2.29 0.65 2.25 1.00 4.00 0.19 -0.25
 Personal integrity 3.41 0.55 3.50 1.00 4.00 -0.94 0.71
 Relational integrity 2.71 0.55 2.75 1.00 4.00 0.27 -0.27
 Moral efficacy 3.05 0.49 3.00 1.00 4.00 -0.28 0.21
Quiet quitting 2.43 0.67 2.33 1.00 5.00 0.51 0.38
 Detachment 2.20 0.74 2.00 1.00 5.00 0.74 0.81
 Lack of initiative 2.40 0.86 2.33 1.00 5.00 0.43 -0.30
 Lack of motivation 2.91 0.94 3.00 1.00 5.00 0.32 -0.51
Job burnout 7.29 2.07 8.00 0.00 10.00 -0.91 0.87
Table 3. Linear regression models with quiet quitting as the dependent variable (N=957).
Table 3. Linear regression models with quiet quitting as the dependent variable (N=957).
Detachment score Lack of initiative score Lack of motivation score
Univariate models Multivariable modela,b Univariate models Multivariable modela,c Univariate models Multivariable modela,d
Coefficient beta 95% CI for beta P-value Coefficient beta 95% CI for beta P-value Coefficient beta 95% CI for beta P-value Coefficient beta 95% CI for beta P-value Coefficient beta 95% CI for beta P-value Coefficient beta 95% CI for beta P-value
Response to moral adversity -0.20 -0.27 to -0.13 <0.001 -0.11 -0.18 to -0.03 0.005 -0.30 -0.38 to -0.22 <0.001 -0.15 -0.23 to -0.06 0.001 -0.37 -0.46 to -0.29 <0.001 -0.31 -0.41 to -0.22 <0.001
Personal integrity -0.27 -0.35 to -0.18 <0.001 -0.10 -0.20 to -0.002 0.046 -0.42 -0.52 to -0.33 <0.001 -0.04 -0.15 to 0.07 0.44 -0.26 -0.37 to -0.15 <0.001 -0.06 -0.19 to 0.07 0.35
Relational integrity -0.30 -0.38 to -0.22 <0.001 -0.15 -0.24 to -0.06 0.002 -0.45 -0.55 to -0.36 <0.001 -0.18 -0.28 to -0.08 0.001 -0.26 -0.37 to -0.15 <0.001 -0.01 -0.13 to 0.11 0.89
Moral efficacy -0.38 -0.47 to -0.29 <0.001 -0.22 -0.34 to -0.11 <0.001 -0.71 -0.82 to -0.61 <0.001 -0.50 -0.63 to -0.37 <0.001 -0.45 -0.57 to -0.34 <0.001 -0.32 -0.47 to -0.17 <0.001
a Multivariable models are adjusted for gender, age, understaffed department, shift work, and work experience. b R2 for the multivariable model = 10.2%, p-value for ANOVA < 0.001. c R2 for the multivariable model = 20.8%, p-value for ANOVA < 0.001. d R2 for the multivariable model = 12.1%, p-value for ANOVA < 0.001. CI: confidence interval.
Table 4. Linear regression models with job burnout as the dependent variable (N=957).
Table 4. Linear regression models with job burnout as the dependent variable (N=957).
Univariate models Multivariable modela,b
Coefficient beta 95% CI for beta P-value Coefficient beta 95% CI for beta P-value
Response to moral adversity -0.60 -0.80 to -0.40 <0.001 -0.65 -0.86 to -0.45 <0.001
Personal integrity 0.38 0.14 to 0.62 0.002 0.22 -0.06 to 0.49 0.12
Relational integrity 0.04 -0.20 to 0.28 0.73 0.10 -0.15 to 0.36 0.43
Moral efficacy 0.24 -0.03 to 0.51 0.08 0.11 -0.21 to 0.43 0.51
a Multivariable model is adjusted for gender, age, understaffed department, shift work, and work experience. b R2 for the multivariable model = 14.4%, p-value for ANOVA < 0.001. CI: confidence interval.
Table 5. Logistic regression models with turnover intention as the dependent variable (N=957), (reference category: low level of turnover intention).
Table 5. Logistic regression models with turnover intention as the dependent variable (N=957), (reference category: low level of turnover intention).
Univariate models Multivariable modela,b
Odds ratio 95% CI for odds ratio P-value Odds ratio 95% CI for odds ratio P-value
Response to moral adversity 0.43 0.35 to 0.53 <0.001 0.39 0.31 to 0.50 <0.001
Personal integrity 0.99 0.79 to 1.25 0.94 1.04 0.77 to 1.42 0.79
Relational integrity 0.80 0.64 to 1.01 0.06 1.12 0.84 to 1.50 0.43
Moral efficacy 0.83 0.64 to 1.08 0.17 1.04 0.73 to 1.50 0.82
a Multivariable model is adjusted for gender, age, understaffed department, shift work, and work experience. b R2 for the multivariable model = 16.8%. CI: confidence interval
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