To the best of our knowledge, this is the first study that estimates the level of quiet quitting among workers with a valid and reliable tool, namely the “Quiet Quitting” Scale. In particular, we measured quiet quitting in a sample of HCWs in Greece including nurses, physicians, dentists, pharmacists, midwives, psychologists, and physiotherapists that have been working in healthcare services. Moreover, we examined the role of type of job and other socio-demographic characteristics on quiet quitting. Additionally, we evaluated the impact of job burnout, and job satisfaction on quiet quitting.
Since the QQS is a newly developed instrument, we performed a validation study to examine the validity and the reliability of the instrument in our sample of HCWs. Our CFA confirmed the three-factor nine-item structure of the QQS (Galanis et al., 2023b) since all goodness-of-fit statistics had excellent values. Therefore, the QQS consists of three factors, i.e. detachment (four items), lack of initiative (three items), and lack of motivation (two items). Additionally, Cronbach’s alpha and McDonald’s Omega values, and test-retest analysis confirmed the reliability of the QQS in our study.
The main finding of our study was that the level of quiet quitting was higher among nurses than physicians and other HCWs. In particular, prevalence of quiet quitting was 67.4% for nurses, 53.8% for physicians, and 40.3% for other HCWs. Moreover, this finding remained even after the elimination of confounders with multivariable linear regression analysis. Also, higher levels of quiet quitting among nurses were identified not only by the QQS but also by the three sub-factors of the scale. Although there are no similar studies on this field, literature suggests that nurses experience higher levels of burnout than other HCWs (Bridgeman et al., 2018). Also, during the COVID-19 pandemic nurses seemed to be at higher risk of experiencing burnout (Gualano et al., 2021). Additionally, the prevalence of burnout, anxiety, stress, depression, and post-traumatic stress within HCWs and especially nurses during the pandemic was high (Saladino et al., 2021; Salari et al., 2020).
Only a recent Gallup survey with a sample of more than 15,000 workers in the USA has estimated by proxy the percentage of quiet quitting among workers (Harter, 2022). In particular, this survey used a 12-items scale to measure employee engagement as the level of employees’ involvement and enthusiasm in their work. Gallup’s survey found that 34% of employees were engaged, 16% were actively disengaged, and 50% were not engaged. The latter were considered by the investigators as “quiet quitters”. The prevalence of quiet quitters within HCWs in our study (57.9%) was similar to Gallup’s survey.
We found that the higher the levels of job burnout were the higher the levels of quiet quitting were also. In a similar way, we identified a negative relationship between job satisfaction and quiet quitting. Although there are no studies that investigate the direct relationship between burnout and quiet quitting several systematic reviews confirm that burnout is associated with other work-related variables, such as absenteeism, turnover, and poor communication with supervisors (Gualano et al., 2021; Johnson et al., 2018). Similarly, literature suggests a strong relationship between work dissatisfaction and turnover intention, job strain, work disengagement (van Diepen et al., 2020; Yildiz et al., 2022). Thus, work-related variables such as burnout and satisfaction seem to be predictors of quiet quitting. Since these variables are modifiable we should put mechanisms in place to support HCWs and improve work environment.
According to our results several socio-demographic variables were associated with quiet quitting. In particular, we found a negative relationship between work experience and quiet quitting. In other words, the level of quiet quitting was higher among younger workers since there was a strong correlation between age and work experience within our HCWs. Gallup’s survey (Harter, 2022) confirms indirectly this finding since scholars found a significant decline in engagement among employees below age 35. In particular, they found that the percentage of engaged workers less than 35 years old decreased by 4% from 2019 to 2022, while the percentage of actively disengaged increased by 6%. Considering the increasing ratio of young workers in healthcare industry, the large percentage of the healthcare workforce close to retirement (Szabo et al., 2020), and the high percentage of quiet quitters among younger workers, policy makers should critically analyze the distribution of HCWs to avoid an overall shortage of them.
Our study found that shift work had a negative impact on quiet quitting. Several studies confirm the negative effect of shift work on work-related variables, such as job burnout, dissatisfaction, and turnover intention especially among nurses (Blytt et al., 2022; Dall’Ora et al., 2023; Jaradat et al., 2017). Since healthcare industry is an occupational sector where most HCWs work in shifts, we should develop and implement interventions to prevent disturbances from shift work among HCWs.
According to our study, HCWs in private sector experienced higher levels of quiet quitting. Studies showed greater dissatisfaction of job control, higher perceived job insecurity, and higher turnover rate among private sector HCWs than those in the public sector (Liu & Cheng, 2018; Margallo-Lana et al., 2001; Yeh et al., 2018). Since work conditions in the public sector seem to be better than in the private sector, we should implement policies to support disadvantaged HCWs in the private sector.
Our study had several limitations. First, we used a convenience sample of HCWs that cannot be representative of HCWs in Greece. For example, our sample included mainly females and HCWs with a MSc/PhD diploma. Further studies with bigger and more representative samples can add valuable data. Second, we collected our data through self-reported questionnaires. We used valid and reliable instruments to collect the information, but information bias is still possible since HCWs may compromise their answers. Third, we measured several variables as potential determinants of quiet quitting but many other variables could be also predictors of the outcome, such as work-life balance, work engagement, remote work, etc. Fourth, we used the QQS for first time in a sample of HCWs. Although the QQS was proven to be valid and reliable in our study, future studies could also examine the psychometric properties of the instrument in other populations and cultures. Fifth, we conducted a cross-sectional study and causal relationships between the independent variables and quiet quitting cannot be established. Furthermore, our study was the first attempt to measure quiet quitting among HCWs. Thus, there is a need for further studies on this field. Moreover, longitudinal studies measuring changes of HCWs’ responses overtime can add valuable information.