Characteristics | N | % |
Gender | ||
Males | 94 | 9.5 |
Females | 898 | 90.5 |
Agea | 42.2 | 9.7 |
Years of work experiencea | 17.2 | 10.2 |
Shift work | ||
No | 320 | 32.3 |
Yes | 672 | 67.7 |
Understaffed department | ||
No | 191 | 19.3 |
Yes | 801 | 80.7 |
Citation: Bernadette M. Pauly, Sana Z. Shahram, Phuc T. H. Dang, Lenora Marcellus, Marjorie MacDonald. Health Equity Talk: Understandings of Health Equity among Health Leaders[J]. AIMS Public Health, 2017, 4(5): 490-512. doi: 10.3934/publichealth.2017.5.490
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Emotional intelligence can be defined as the capacity of individuals to monitor their own feelings and others' feelings, and manage emotions in themselves and in their relationships [1]. There are two categories of attributes of emotional intelligence in professional nursing practice: personal and social [2]. Personal attributes refer to self-awareness and self-management, while social attributes refer to relationship management and social awareness. Since there is always an exchange of experiences, actions, and emotions between nurses and their patients, emotional intelligence plays a central role in all aspects of nursing practice [3], [4]. Thus, previous studies have showed the positive impact of nurses' emotional intelligence on patient outcomes, patient satisfaction, and quality service provisions [5]–[7]. On the other hand, low levels of emotional intelligence among nurses lead to burnout, work-related stress and work accidents, threaten the relationship between nurses and patients, increase patients' poor outcomes, reduce nurses' physical and mental well-being, and increase turnover intention [8]–[13].
Several systematic reviews and meta-analyses identified moderate to high levels of turnover intention and job burnout and reduced work satisfaction among nurses [14]–[20]. For example, a meta-analysis with 23,140 nurses found that the overall prevalence of turnover intention was 27.7% [17]. Also, a meta-analysis of 18,935 nurses found that the pooled prevalence of a lack of personal accomplishment, depersonalization, and emotional exhaustion was 15.2%, 12.6%, and 34.1%, respectively [15]. Moreover, working conditions worsened during the COVID-19 pandemic with nurses experiencing even greater levels of burnout, turnover intention, and dissatisfaction. The National Council of State Boards of Nursing reported that almost 100,000 nurses left their work during the pandemic in the USA, while 25% of all nurses intend to leave the workforce over the next five years [21]. Additionally, in the wake of the pandemic, the “quiet quitting” phenomenon started to threaten healthcare services, where employees offer the minimum of their capacity [22]. Interestingly, recent evidence suggests that a great percentage of nurses can be considered quiet quitters, and nurses acting as quiet quitters are found more often than other healthcare workers [23]. Quiet quitting has already been recognized as a significant threat for healthcare organizations, since the healthcare professionals may reduce their efforts to avoid job burnout, physical and mental outcomes, and occupational stress [22],[24]. Moreover, a recent study highlighted the negative consequences of quiet quitting for healthcare professionals and organizations as scholars found that quiet quitting increased turnover intention among nurses [25].
Several studies have investigated the relationship between emotional intelligence and work-related variables among nurses. In particular, high emotional intelligence has been linked to higher job satisfaction [9],[26],[27], higher work performance [27]–[31], higher professional success [32], less job burnout [9],[33], less occupational stress [31], and lower turnover intentions [34],[35]. In other words, high emotional intelligence is associated with the most beneficial work-related outcomes (i.e. work performance and job satisfaction), while low emotional intelligence is associated with the most negative work-related outcomes (i.e. job burnout, occupational stress, and turnover intention). Overall, literature suggests the protective effect of emotional intelligence against the adverse effects of the nursing profession.
In this context, emotional intelligence is an essential skill for nurses, and the impact of it on work-related variables should be examined in depth. There are only two studies, one in Spain and one in South Korea, that have investigated the relationship between emotional intelligence and job burnout [9],[33]. Moreover, another study in Taiwan investigated the relationship between emotional intelligence and turnover intention [35]. Additionally, there are no studies on whether emotional intelligence affects quiet quitting. Herein, our aim was to assess the impact of nurses' emotional intelligence on quiet quitting. Moreover, we examined the effect of emotional intelligence on turnover intention and job burnout among nurses.
We performed a cross-sectional study during November 2023. We used a convenience sample and approached nurses in several ways. First, we used Google forms to create our online study questionnaire, and then we posted it on social media. Also, we collected data via paper and pencil to approach nurses that did not use social media frequently. Our nurses were required to work at least two years in a clinical setting and be Greek-speaking. We calculated the sample size of nurses as 652 by applying the following parameters: (a) a low effect size (f2=0.02) between our study variables, (b) nine independent variables, (c) 95% level of confidence, and (d) 5% margin of error.
Demographic variables included gender, age, work experience, shift work, and understaffed departments.
Moreover, our study included the followed instruments:
The Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) [36]: This 30-item measure assesses trait emotional intelligence. Participants respond on a 7-point Likert scale from 1 (completely disagree) to 7 (completely agree). TEIQue-SF provides scores on four factors (well-being, self-control, emotionality, sociability), and global trait emotional intelligence. Scores range on a scale from 1 to 7. Higher values indicate higher levels of emotional intelligence. We used the valid Greek version of the TEIQue-SF [37]. In our study, Cronbach's α for global trait emotional intelligence well-being, self-control, emotionality, and sociability was 0.882, 0.786, 0.639, 0.605, and 0.740, respectively.
The Quiet Quitting Scale (QQS) [38]: This 9-item measure evaluates three factors: detachment, lack of motivation, and lack of initiative. Scores range on a scale from 1 (low level of quiet quitting) to 5 (high level of quiet quitting). According to the developers of the QQS, a total score greater than 2.06 refers to quiet quitters [39]. The QQS has demonstrated very good psychometric properties [23]. Internal reliability analysis indicated acceptable Cronbach's α for QQS as well as for all three factors: 0.852 for the QQS; 0.761 for the factor “detachment”; 0.728 for the factor “lack of initiative”; and 0.747 for the factor “lack of motivation”.
The single-item burnout measure [40]: This one-item measure assesses job burnout on a scale from 0 (lower levels of burnout) to 10 (higher levels of burnout). A study including nurses in Greece has proven that the tool is valid [41].
The turnover intention scale [42]: A single question (“How often have you seriously considered leaving your current job?”) estimates turnover intention among workers. Answers are on a 6-point Likert scale from 1 (rarely) to 6 (extremely often). Workers with values ≥ 4 belong to a group with a high level of turnover intention, while workers with values from 0–3 belong to a group with a low level of turnover intention.
We followed the Declaration of Helsinki to conduct our study [43]. Moreover, we had approval from the Ethics Committee of the National and Kapodistrian University of Athens, Faculty of Nursing (464, October 2023). We invited nurses to participate in an anonymous and voluntary way. We did not offer any compensation for participation.
Categorical variables are presented with numbers and percentages. Continuous variables are presented with mean, standard deviation, and median. We assessed the distribution of continuous variables with the Kolmogorov-Smirnov test, Q-Q plots, skewness values, and kurtosis values. Continuous variables followed normal distribution. Considering that quiet quitting and job burnout were continuous variables that followed normal distribution, we applied linear regression analysis. Also, considering that turnover intention was a dichotomous variable (high levels versus low levels), we applied logistic regression analysis. We performed univariate and multivariable regression analysis to eliminate confounding that was caused by the demographic variables. We presented unadjusted and adjusted coefficients beta, 95% confidence intervals (CI), p-values, and R2 for the linear regression models. We presented odds ratios (OR), 95% CIs, p-values, and R2 for the logistic regression models. P-values less than 0.05 were considered as statistically significant results. IBM SPSS 21.0 (IBM Corp. Released 2012. The IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.) was used for statistical analysis.
The detailed descriptive statistics for the demographic variables are shown in Table 1. The mean age of our nurses was 42.2 years.
Characteristics | N | % |
Gender | ||
Males | 94 | 9.5 |
Females | 898 | 90.5 |
Agea | 42.2 | 9.7 |
Years of work experiencea | 17.2 | 10.2 |
Shift work | ||
No | 320 | 32.3 |
Yes | 672 | 67.7 |
Understaffed department | ||
No | 191 | 19.3 |
Yes | 801 | 80.7 |
Note: a mean, standard deviation.
Descriptive statistics for our study scales are shown in detail in Table 2. It can be seen that emotional intelligence, well-being, and emotionality were higher than self-control and sociability. Regarding quiet quitting, a lack of motivation was shown to be higher than a lack of initiative and detachment.
Among our nurses, 74.4% (n = 738) were considered as quiet quitters, while more than half of our nurses (51.6% [n = 512]) reported a high level of turnover intention.
Project | Mean | Standard deviation | Median | Kurtosis | Skewness |
Emotional intelligence | 4.89 | 0.71 | 4.97 | -0.17 | -0.39 |
Well-being | 5.25 | 0.99 | 5.33 | 0.48 | -0.73 |
Self-control | 4.65 | 0.90 | 4.67 | -0.18 | -0.24 |
Emotionality | 4.94 | 0.78 | 5.12 | 0.29 | -0.55 |
Sociability | 4.45 | 1.01 | 4.50 | -0.37 | -0.25 |
Quiet quitting | 2.55 | 0.74 | 2.44 | 0.30 | 0.60 |
Lack of motivation | 3.02 | 0.94 | 3.00 | -0.41 | 0.28 |
Detachment | 2.35 | 0.82 | 2.25 | 0.63 | 0.73 |
Lack of initiative | 2.52 | 0.92 | 2.33 | -0.45 | 0.42 |
Job burnout | 7.54 | 1.88 | 8.00 | 1.19 | -0.98 |
After controlling for demographic variables, the multivariable linear regression model indicated that self-control and emotionality reduced detachment. Moreover, emotionality and sociability reduced the lack of initiative, while sociability, self-control, emotionality, and well-being reduced the lack of motivation. Detailed results from the linear regression analyses are shown in Table 3.
Project | Lack of motivation |
Lack of initiative |
Detachment |
||||||
Multivariable modela,b |
Multivariable modela,c |
Multivariable modela,d |
|||||||
Beta coefficient | 95% CI for beta | P-value | Beta coefficient | 95% CI for beta | P-value | Beta coefficient | 95% CI for beta | P-value | |
Well-being | -0.19 | -0.26 to -0.12 | <0.001 | -0.07 | -0.14 to -0.004 | 0.038 | -0.05 | -0.11 to 0.01 | 0.116 |
Self-control | -0.09 | -0.17 to -0.02 | 0.017 | -0.07 | -0.14 to 0.001 | 0.055 | -0.16 | -0.23 to -0.09 | <0.001 |
Emotionality | -0.11 | -0.19 to -0.03 | 0.007 | -0.18 | -0.26 to -0.10 | <0.001 | -0.18 | -0.25 to -0.10 | <0.001 |
Sociability | -0.10 | -0.17 to -0.04 | 0.002 | -0.21 | -0.27 to -0.15 | <0.001 | -0.02 | -0.08 to 0.04 | 0.512 |
Note: a The multivariable models were adjusted for the demographic variables: the p-value for ANOVA was <0.001 in all models; b R2 = 12.6%; c R2 = 20.7%; d R2 = 16.2%; CI: confidence interval.
Similarly, we found a negative relationship between emotional intelligence and turnover intention. As shown in Table 4, the multivariable logistic regression model identified that the effect of well-being and self-control on turnover intention was significantly negative. Thus, nurses with higher levels of emotional intelligence were less probable to leave their work. R2 for the final multivariable model was 14.9%.
Project | Multivariable modela |
||
Odds ratio | 95% confidence interval for odds ratio | P-value | |
Well-being | 0.79 | 0.66 to 0.94 | 0.007 |
Self-control | 0.58 | 0.48 to 0.71 | <0.001 |
Emotionality | 0.92 | 0.75 to 1.13 | 0.420 |
Sociability | 0.97 | 0.82 to 1.13 | 0.669 |
Note: a The multivariable model was adjusted for the demographic variables.
Additionally, we found a negative relationship between emotional intelligence and job burnout. In particular, we found that well-being and self-control were associated with decreased job burnout. R2 for the final multivariable model was 14.2%. Detailed results from the linear regression analyses are presented in Table 5.
Project | Multivariable modela |
||
Coefficient beta | 95% confidence interval for beta | P-value | |
Well-being | -0.19 | -0.34 to -0.06 | 0.006 |
Self-control | -0.16 | -0.31 to -0.004 | 0.044 |
Emotionality | -0.04 | -0.21 to 0.13 | 0.629 |
Sociability | -0.08 | -0.21 to 0.05 | 0.202 |
Note: a The multivariable model was adjusted for the demographic variables.
Our study was the first that examined the effect of nurses' emotional intelligence on quiet quitting. Additionally, since the literature is limited concerning the relationship between emotional intelligence, turnover intention, and job burnout among nurses, we examined these relationships. In our study, we assessed the direct effect of emotional intelligence after adjusting for several demographic variables. Our multivariable models demonstrated significant negative relationships between emotional intelligence, turnover intention, job burnout, and quiet quitting. In particular, our results suggested that emotional intelligence reduced quiet quitting, turnover intention, and job burnout in our nurses.
We found that nurses with higher levels of emotional intelligence experienced lower levels of quiet quitting. Although there is a dearth of literature regarding the relation between emotional intelligence and quiet quitting, evidence suggests a positive impact of emotional intelligence on several work-related variables. In particular, several studies showed that emotional intelligence had a positive effect on work performance [27]–[31], job satisfaction [9],[26],[27], organizational commitment [44], and clinical competence [45] among nurses. Since nurses are usually exposed to high levels of responsibility, stress, and emotional demands, emotional intelligence is a crucial tool to achieve the most beneficial outcomes. Emotional intelligence favors a good therapeutic relationship between nurses and patients, leading to greater adaptability and more positive attitudes, and protects nurses from occupational stress and burnout [46],[47]. In this context, nurses experience greater satisfaction with their work and improve their work performance [48]–[50]. A recent scoping review has identified that nurses are interested in enhancing their emotional intelligence because they recognize that emotional skills are necessary to better rationalize their experiences and emotions in the workplace [51]. Moreover, emotional intelligence improves nurses' ability to deeper understand their profession and show more spiritual maturity [52],[53]. Thus, more emotionally intelligent nurses can also acquire other intrinsic values, e.g., resilience, responsibility, self-awareness, self-management, and self-reflection [54]–[56].
In our study, more emotionally intelligent nurses experienced less burnout. Soto-Rubio et al. confirmed our findings by examining a sample of 125 nurses in Spain [9]. In a similar way, Kwon and Kim recognized the negative relationship between emotional intelligence and job burnout in a sample of nurses working at mental hospitals in South Korea [33]. A recent scoping review has emphasized the relevance of emotional intelligence in burnout prevention and emotional exhaustion [57]. Nurses with high emotional intelligence can create a positive work environment by incorporating spiritual motivation in their clinical practice [53],[56]. Moreover, emotionally intelligent nurses can identify the emotions of themselves, their patients, and their colleagues [51]. Thus, these nurses are more capable of coping with daily work challenges and adapting more effectively to work difficulties. In this context, nurses can handle excessive emotional and work-related expectations, thus avoiding high levels of job burnout [8],[9].
As a result of our study, emotional intelligence was a significant predictor of turnover intention. In particular, more emotionally intelligent nurses expressed lower intention to leave their job. Wang et al. confirmed our results as they found a significant negative relationship between emotional intelligence and turnover intention in a sample of nurses in Taiwan [35]. In a similar way, Al-Hamdan et al. examined the attitudes of 280 nurses in Jordan and found a positive correlation between emotional intelligence and intent to stay [34]. Our findings are in accordance with existing literature as studies found that leaders' emotional intelligence, as perceived by nurses, had a positive effect on turnover intention [58],[59]. Since turnover intention among healthcare workers has increased dramatically after the COVID-19 pandemic, identification of predictors of turnover intention is crucial to understanding the way that healthcare workers consider their future in the industry. For instance, one in four nurses in the USA intends to leave their work over the next five years [21]. Similarly, a recent survey in the USA revealed that 20% of physicians intend to leave primary care over the next three years [60].
Our study had some limitations. First, we cannot establish a causal relationship between our study variables since we performed a cross-sectional study. Longitudinal studies could add more information on our research questions. Second, we used a convenience sample and, thus, we cannot generalize our results. Future research should include random, stratified, and representative samples to improve our knowledge. Third, we used valid instruments to measure our study variables. However, our tools were self-reported and information bias is likely. Fourth, we performed our study in a sample of nurses in Greece with particular characteristics. Scholars should conduct studies in other cultural and contextual settings to shed more light on this issue. Finally, we constructed our multivariable models by eliminating several confounders, but several other variables can act as confounders. Thus, future studies should eliminate more confounders to extract more valid results.
Overall, our findings support the protective role of emotional intelligence against quiet quitting, turnover intention, and job burnout among nurses. However, considering the limitations of our study and the limited literature regarding our research questions, further studies should be conducted to get more valid results. Since the nursing profession demands mentally and physically strong nurses, high levels of emotional intelligence are crucial to maintain their mental and emotional well-being. Our study informs nurse managers and healthcare policymakers about the positive impact of emotional intelligence on work-related outcomes, such as turnover intention, quiet quitting, and job burnout. Therefore, nurse managers and healthcare policymakers should pay attention to the emotional intelligence of nurses by applying the appropriate interventions to develop emotional intelligence skills in nurses. For example, training programs focused on emotional intelligence, including interpersonal relationships, coping strategies, stress management, and relaxation techniques, may empower the psychological resources of nurses and improve their emotional intelligence. Appropriate emotional intelligence skills should be developed to improve nurses' commitment to their organization and retention.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
[1] | Crombie I, Irvine L, Elliott L, et al. (2005) Closing the health inequalities gap: An international perspective. Copenhagen: World Health Organization. |
[2] | Commission on the Social Determinants of Health (2008) Closing the gap in a generation: achieving health equity through action on the social determinants of health. Geneva: World Health Organization. |
[3] | Whitehead M, Dahlgren G (2006) Levelling up (part 1): A discussion paper on concepts and principles for tackling social inequities in health. Copenhagen: World Health Organization. |
[4] |
Baum F, Begin M, Houweling T, et al. (2009) Changes not for the fainthearted: reorienting health care systems toward health equity through action on the social determinants of health. Am J Public Health 99: 1967. doi: 10.2105/AJPH.2008.154856
![]() |
[5] |
Stankiewicz A, Herel M, DesMeules M (2015) Report summary--Rio political declaration on social determinants of health: a snapshot of Canadian actions 2015. Health Promot Chronic Dis Prev Can: Res Policy Pract 35: 113-114. doi: 10.24095/hpcdp.35.7.03
![]() |
[6] | National Collaborating Centre for Determinants of Health (2013) Let's talk: health equity. Antigonish, NS National Collaborating Centre for Determinants of Health, St. Francis Xavier University. |
[7] |
Sadana R, Blas E (2013) What can public health programs do to improve health equity? Public Health Rep 128: 12-20. doi: 10.1177/00333549131286S303
![]() |
[8] |
Brassolotto J, Raphael D, Baldeo N (2014) Epistemological barriers to addressing the social determinants of health among public health professionals in Ontario, Canada: a qualitative inquiry. Crit Public Health 24: 321-336. doi: 10.1080/09581596.2013.820256
![]() |
[9] | Varcoe C, Pauly BM, Laliberte S (2012) Intersectionality, social justice and influencing policy. In: Hankivsky O. Health inequities in Canada: Intersectional frameworks and practices. Vancouver: UBC Press, 331-348. |
[10] |
Raphael D (2003) Barriers to addressing the societal determinants of health: Public health units and poverty in Ontario, Canada. Health Promot Int 18: 397-405. doi: 10.1093/heapro/dag411
![]() |
[11] |
Graham H (2004) Social determinants and their unequal distribution: Clarifying policy understandings. Milbank Q 82: 101-124. doi: 10.1111/j.0887-378X.2004.00303.x
![]() |
[12] | Braveman P (2014) What are health disparities and health equity? We need to be clear. Public Health Rep 129: 5-8. |
[13] | World Health Organization (2017) What are the social determinants of health? |
[14] |
Bryant T, Raphael D, Schrecker T, et al. (2011) Canada: A land of missed opportunity for addressing the social determinants of health. Health Policy 101: 44-58. doi: 10.1016/j.healthpol.2010.08.022
![]() |
[15] | Canadian Institutes of Health Research (2003) The future of public health in Canada: Developing a public health system for the 21st century. Ottawa: Author. |
[16] | Di Ruggiero E, Frank J, Moloughney B (2004) Strengthen Canada's public health system now. Can J Public Health 95: 5-11. |
[17] | Select Standing Committee on Health (2004) The path to health and wellness: Making British Columbians healthier by 2010. Victoria, BC: British Columbia Legislative Assembly. |
[18] | Bill C (2002) The standing senate committee on social affairs, science and technology. The health of Canadians – the federal role. Ottawa: Government of Canada. |
[19] | Ministry of Health Services Population Health and Wellness (2005) Public health renewal in British Columbia: an overview of core functions in public health. Victoria BC: Ministry of Health Services. |
[20] | Ministry of Health Services Population Health and Wellness (2005) A framework for core functions in public health. Victoria BC: Ministry of Health Services. |
[21] | British Columbia Ministry of Health (2013) Promote, protect, prevent: Our health begins here. B.C's Guiding Framework for Public Health. In: Population Health and Wellness BCMoHS. Ministry of Health. Victoria, BC. |
[22] |
Braveman P (2006) Health disparities and health equity: concepts and measurement. Annu Rev Public Health 27: 167. doi: 10.1146/annurev.publhealth.27.021405.102103
![]() |
[23] |
Smith MJ (2015) Health equity in public health: clarifying our commitment. Public Health Ethics 8: 173. doi: 10.1093/phe/phu042
![]() |
[24] | Brassolotto J, Raphael D, Baldeo N (2013) Epistemological barriers to addressing the social determinants of health among public health professionals in Ontario, Canada: A qualitative inquiry. Crit Public Health 24: 321-338. |
[25] |
Pauly BM, MacDonald M, Hancock T, et al. (2013) Reducing health inequities: The contribution of core public health services in BC. BMC Public Health 13: 550. doi: 10.1186/1471-2458-13-550
![]() |
[26] |
Walby S (2007) Complexity theory, systems theory, and multiple intersecting social inequalities. Philos Soc Sci 37: 449-470. doi: 10.1177/0048393107307663
![]() |
[27] | Walby S, Armstrong J, Strid S (2012) Intersectionality: Multiple inequalities in social theory. Socioogy 46: 224-240. |
[28] | Walby S (2009) Theorizing Multiple Social Systems. Globalization and Inequalities: Complexity and Contested Modernities. London: Sage. |
[29] | Hankivsky O, Cormier R (2009) Intersectionality: Moving women's health research and policy forward. Vancouver: Women's Health Research Network. |
[30] |
Hankivsky O, Reid C, Cormier R, et al. (2010) Exploring the promises of intersectionality for advancing women's health research. Int J Inequity Health 9: 5. doi: 10.1186/1475-9276-9-5
![]() |
[31] |
McCall L (2005) The complexity of intersectionality. J Women Cultur Soc 30: 1771-1800. doi: 10.1086/426800
![]() |
[32] | McGibbon E, MacPherson C (2011) Applying intersectionality and complexity theory to address the social determinants of women's health. Women's Health Urban Life 10: 59-86. |
[33] | Ostlin P, Schrecker T, Sadana R (2010) Priorities for research on equity and health: Implications for global and national priority setting and the role of WHO to take the health equity research agenda forward. Final report. |
[34] |
Boeije H (2002) A purposeful approach to the constant comparative method in the analysis of qualitative interviews. Qual Quant 36: 391-409. doi: 10.1023/A:1020909529486
![]() |
[35] | Fram SM (2013) The constant comparative analysis method outside of grounded theory. Qual Rep 18: 1. |
[36] |
Marent B, Forest R, Nowalk P (2012) Theorizing participation in health promotion: A literature review. Soc Theory Health 10: 188-207. doi: 10.1057/sth.2012.2
![]() |
[37] | MacDonald M, Mullett J (2008) Dilemmas in health promotion evaluation: Participation and empowerment. In: Potvin L, McQueen DV, Health promotion evaluation practices in the Americas. New York: Springer, 149-178. |
[38] | Young IM (1990) Justice and the politics of difference. Princeton, N.J.: Princeton University Press. |
[39] | Fraser N (2007) Re-framing justice in a globalizing world. In: Lovel T. (Mis)recognition, social inequality and social justice: Nancy Fraser and Pierre Bourdieu. New York: Routledge. |
[40] |
Carey G, Crammond B, De Leeuw E (2015) Towards health equity: a framework for the application of proportionate universalism. Int J Equity Health 14: 8. doi: 10.1186/s12939-015-0134-6
![]() |
[41] |
Quesada J, Hart L, Bourgois P (2011) Structural vulnerabilty and health: Latino Migrant Laboreres in the United States. Med Anthropol Q 30: 339-362. doi: 10.1080/01459740.2011.576725
![]() |
[42] |
Bauer GR (2014) Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Soc Sci Med 110: 10-17. doi: 10.1016/j.socscimed.2014.03.022
![]() |
[43] |
Priest N, Paradies Y, Trenerry B, et al. (2013) A systematic review of studies examining the relationship between reported racism and health and wellbeing for children and young people. Soc Sci Med 95: 115-127. doi: 10.1016/j.socscimed.2012.11.031
![]() |
[44] | Paradies Y, Jehonathan B, Denson N, et al. (2015) Racism as a determinant of health: A systematic review and meta-analysis. PLOS One 10: 85. |
[45] | Benoit C, Shumka L (2009) Gendering the population health perspective: Fundamental determinants of women's health. Vancouver, BC.: Women's Health Research Network. |
[46] | Powers M, Faden R (2006) Social justice: the moral foundations of public health and health policy: Oxford University Press, USA. |
[47] |
Hankivsky O (2012) Women's health, men's health, and gender and health: implications of intersectionality. Soc Sci Med 74: 1712-1720. doi: 10.1016/j.socscimed.2011.11.029
![]() |
[48] |
Socías ME, Koehoorn M, Shoveller J (2016) Gender inequalities in access to health care among adults living in British Columbia, Canada. Women's Health Issues 26: 74-79. doi: 10.1016/j.whi.2015.08.001
![]() |
[49] | Marmot M, Allen JJ (2014) Social determinants of health equity. Slov J Public Health 51: 1-4. |
[50] | Jackson SF, Birn AE, Fawcett SB, et al. (2013) Synergy for health equity: integrating health promotion and social determinants of health approaches in and beyond the Americas. Rev Panam Salud Publica 34: 473-480. |
[51] | Solar O, Irwin A (2010) A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). Geneva: World Health Organization. |
[52] | Shareck M, Frohlich KL, Poland B (2013) Reducing inequities in health through settings related interventions - a conceptual framework. Glob Health Promot 20: 39-52. |
1. | Petros Galanis, Aglaia Katsiroumpa, Ioannis Moisoglou, Konstantina Derizioti, Parisis Gallos, Maria Kalogeropoulou, Vasiliki Papanikolaou, Emotional Intelligence as Critical Competence in Nurses’ Work Performance: A Cross-Sectional Study, 2024, 12, 2227-9032, 1936, 10.3390/healthcare12191936 | |
2. | Gerlinde Weilguny-Schöfl, Quiet Quitting, 2024, 29, 0949-7323, 45, 10.1007/s00735-024-1906-2 | |
3. | Hanan A. Alkorashy, Aisha F. Basheer, Hanem F. Mohamed, The Moderating Role of Emotional Intelligence on the Relationship Between Nurses’ Preparedness to Care for COVID-19 Patients and Their Quality of Work Life, 2024, 14, 2076-328X, 1166, 10.3390/bs14121166 | |
4. | Duo Zhang, Haoyang Zheng, Ying Zeng, Xiaoyan Yu, Ya Zhao, Yong Gan, Xichen Chai, Wencan Cheng, Zelin Chen, Yanrong Zhou, Psychometric properties of the Chinese version of the Quiet Quitting Scale, 2025, 24, 1472-6955, 10.1186/s12912-025-02921-4 | |
5. | Ruijia Geng, Xiulin Geng, Sujuan Geng, Identifying Key Antecedents of Quiet Quitting Among Nurses: A Cross‐Profession Meta‐Analytic Review, 2025, 0309-2402, 10.1111/jan.16934 | |
6. | Vasiliki Georgousopoulou, Maria Amanatidou, Pinelopi Vlotinou, Eleni Lahana, Anna Tsiakiri, Ioannis Koutelekos, Eleni Koutra, Georgios Manomenidis, The Role of Organizational Culture and Emotional Intelligence: Enhancing Healthcare Professionals’ Job Satisfaction, 2025, 14, 2076-0760, 286, 10.3390/socsci14050286 | |
7. | Sushmita Bordoloi, Akshay Sharma, Rajib Lochan Dhar, Leveraging SOR theory to understand quiet quitting: systematic insights from existing research, 2025, 2198-1620, 10.1007/s11301-025-00518-4 | |
8. | Chiara Bernuzzi, Giulia Paganin, Sara Petrilli, Simona Margheritti, Old trends in new clothing? Exploring the quiet quitting phenomenon through a scoping review, 2025, 1046-1310, 10.1007/s12144-025-07920-x | |
9. | Harika Şen, A Systematic Review of Quiet Quitting in the Health Sector, 2025, 22, 2791-9781, 391, 10.26466/opusjsr.1659559 |
Characteristics | N | % |
Gender | ||
Males | 94 | 9.5 |
Females | 898 | 90.5 |
Agea | 42.2 | 9.7 |
Years of work experiencea | 17.2 | 10.2 |
Shift work | ||
No | 320 | 32.3 |
Yes | 672 | 67.7 |
Understaffed department | ||
No | 191 | 19.3 |
Yes | 801 | 80.7 |
Note: a mean, standard deviation.
Project | Mean | Standard deviation | Median | Kurtosis | Skewness |
Emotional intelligence | 4.89 | 0.71 | 4.97 | -0.17 | -0.39 |
Well-being | 5.25 | 0.99 | 5.33 | 0.48 | -0.73 |
Self-control | 4.65 | 0.90 | 4.67 | -0.18 | -0.24 |
Emotionality | 4.94 | 0.78 | 5.12 | 0.29 | -0.55 |
Sociability | 4.45 | 1.01 | 4.50 | -0.37 | -0.25 |
Quiet quitting | 2.55 | 0.74 | 2.44 | 0.30 | 0.60 |
Lack of motivation | 3.02 | 0.94 | 3.00 | -0.41 | 0.28 |
Detachment | 2.35 | 0.82 | 2.25 | 0.63 | 0.73 |
Lack of initiative | 2.52 | 0.92 | 2.33 | -0.45 | 0.42 |
Job burnout | 7.54 | 1.88 | 8.00 | 1.19 | -0.98 |
Project | Lack of motivation |
Lack of initiative |
Detachment |
||||||
Multivariable modela,b |
Multivariable modela,c |
Multivariable modela,d |
|||||||
Beta coefficient | 95% CI for beta | P-value | Beta coefficient | 95% CI for beta | P-value | Beta coefficient | 95% CI for beta | P-value | |
Well-being | -0.19 | -0.26 to -0.12 | <0.001 | -0.07 | -0.14 to -0.004 | 0.038 | -0.05 | -0.11 to 0.01 | 0.116 |
Self-control | -0.09 | -0.17 to -0.02 | 0.017 | -0.07 | -0.14 to 0.001 | 0.055 | -0.16 | -0.23 to -0.09 | <0.001 |
Emotionality | -0.11 | -0.19 to -0.03 | 0.007 | -0.18 | -0.26 to -0.10 | <0.001 | -0.18 | -0.25 to -0.10 | <0.001 |
Sociability | -0.10 | -0.17 to -0.04 | 0.002 | -0.21 | -0.27 to -0.15 | <0.001 | -0.02 | -0.08 to 0.04 | 0.512 |
Note: a The multivariable models were adjusted for the demographic variables: the p-value for ANOVA was <0.001 in all models; b R2 = 12.6%; c R2 = 20.7%; d R2 = 16.2%; CI: confidence interval.
Project | Multivariable modela |
||
Odds ratio | 95% confidence interval for odds ratio | P-value | |
Well-being | 0.79 | 0.66 to 0.94 | 0.007 |
Self-control | 0.58 | 0.48 to 0.71 | <0.001 |
Emotionality | 0.92 | 0.75 to 1.13 | 0.420 |
Sociability | 0.97 | 0.82 to 1.13 | 0.669 |
Note: a The multivariable model was adjusted for the demographic variables.
Project | Multivariable modela |
||
Coefficient beta | 95% confidence interval for beta | P-value | |
Well-being | -0.19 | -0.34 to -0.06 | 0.006 |
Self-control | -0.16 | -0.31 to -0.004 | 0.044 |
Emotionality | -0.04 | -0.21 to 0.13 | 0.629 |
Sociability | -0.08 | -0.21 to 0.05 | 0.202 |
Note: a The multivariable model was adjusted for the demographic variables.
Characteristics | N | % |
Gender | ||
Males | 94 | 9.5 |
Females | 898 | 90.5 |
Agea | 42.2 | 9.7 |
Years of work experiencea | 17.2 | 10.2 |
Shift work | ||
No | 320 | 32.3 |
Yes | 672 | 67.7 |
Understaffed department | ||
No | 191 | 19.3 |
Yes | 801 | 80.7 |
Project | Mean | Standard deviation | Median | Kurtosis | Skewness |
Emotional intelligence | 4.89 | 0.71 | 4.97 | -0.17 | -0.39 |
Well-being | 5.25 | 0.99 | 5.33 | 0.48 | -0.73 |
Self-control | 4.65 | 0.90 | 4.67 | -0.18 | -0.24 |
Emotionality | 4.94 | 0.78 | 5.12 | 0.29 | -0.55 |
Sociability | 4.45 | 1.01 | 4.50 | -0.37 | -0.25 |
Quiet quitting | 2.55 | 0.74 | 2.44 | 0.30 | 0.60 |
Lack of motivation | 3.02 | 0.94 | 3.00 | -0.41 | 0.28 |
Detachment | 2.35 | 0.82 | 2.25 | 0.63 | 0.73 |
Lack of initiative | 2.52 | 0.92 | 2.33 | -0.45 | 0.42 |
Job burnout | 7.54 | 1.88 | 8.00 | 1.19 | -0.98 |
Project | Lack of motivation |
Lack of initiative |
Detachment |
||||||
Multivariable modela,b |
Multivariable modela,c |
Multivariable modela,d |
|||||||
Beta coefficient | 95% CI for beta | P-value | Beta coefficient | 95% CI for beta | P-value | Beta coefficient | 95% CI for beta | P-value | |
Well-being | -0.19 | -0.26 to -0.12 | <0.001 | -0.07 | -0.14 to -0.004 | 0.038 | -0.05 | -0.11 to 0.01 | 0.116 |
Self-control | -0.09 | -0.17 to -0.02 | 0.017 | -0.07 | -0.14 to 0.001 | 0.055 | -0.16 | -0.23 to -0.09 | <0.001 |
Emotionality | -0.11 | -0.19 to -0.03 | 0.007 | -0.18 | -0.26 to -0.10 | <0.001 | -0.18 | -0.25 to -0.10 | <0.001 |
Sociability | -0.10 | -0.17 to -0.04 | 0.002 | -0.21 | -0.27 to -0.15 | <0.001 | -0.02 | -0.08 to 0.04 | 0.512 |
Project | Multivariable modela |
||
Odds ratio | 95% confidence interval for odds ratio | P-value | |
Well-being | 0.79 | 0.66 to 0.94 | 0.007 |
Self-control | 0.58 | 0.48 to 0.71 | <0.001 |
Emotionality | 0.92 | 0.75 to 1.13 | 0.420 |
Sociability | 0.97 | 0.82 to 1.13 | 0.669 |
Project | Multivariable modela |
||
Coefficient beta | 95% confidence interval for beta | P-value | |
Well-being | -0.19 | -0.34 to -0.06 | 0.006 |
Self-control | -0.16 | -0.31 to -0.004 | 0.044 |
Emotionality | -0.04 | -0.21 to 0.13 | 0.629 |
Sociability | -0.08 | -0.21 to 0.05 | 0.202 |