Emotional Labour, Job Satisfaction, and Burnout in Medical Technicians: Insights from the Korea Veterans Health Service

doi:10.21203/rs.3.rs-4958831/v1

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Research Article

Emotional Labour, Job Satisfaction, and Burnout in Medical Technicians: Insights from the Korea Veterans Health Service

https://doi.org/10.21203/rs.3.rs-4958831/v1

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Background

Previous research on emotional labour, job satisfaction, and burnout in hospitals has primarily focused on nurses or treated medical technicians as a single occupational group. However, recognising the diverse experiences within different medical technician roles necessitates a more nuanced analysis. This study aims to determine the correlation among emotional labour, job satisfaction, and burnout in medical technicians and provide foundational data for human resource management.

Methods

Participants included 570 medical technologists, radiology technologists, physical therapists, and dental hygienists working in six hospitals under the Korea Veterans Health Service in July 2022. A standardised self-administered questionnaire was used, with 290 participants responding, yielding a response rate of 50.9%. In Model I, burnout was the dependent variable, while emotional labour, job satisfaction, and subjective health status were the independent variables. Model II considered participants’ sociodemographic characteristics and factors influencing their current job choice or potential job changes, building on Model I.

Results

Factors influencing burnout varied across medical technician occupations. In Model II, burnout was significantly affected by medical technologists’ job satisfaction (B=-0.203, P = 0.014), radiology technologists’ emotional labour (B = 0.546, P = 0.024), physical therapists’ emotional labour (B = 0.466, P < 0.001) and subjective health status (B = 3.234, P = 0.029), and dental hygienists’ subjective health status (B = 5.788, P = 0.030). These results highlight the need for tailored management strategies and further research.

Conclusion

Given the variation in factors significantly affecting burnout among occupational groups, tailored interventions, and detailed research specific to each group are necessary.

burnout

emotional labour

job satisfaction

medical technicians

subjective health status

The hospital industry is evolving to improve customer-centric medical services and enhance patient satisfaction. Hospital employees must consider patients’ diverse needs, viewpoints, and emotions more extensively than workers in other sectors [1]. This process, termed emotional labour, involves consciously regulating facial expressions or gestures to meet organisational demands, distinguishing it from physical and mental labour [2]. According to the Korea Employment Information Service, emotional labour intensity among workers in 730 major occupations ranks dental hygienists 14th and radiology technologists 95th among the top 100 occupations requiring high levels of emotional labour [3].

Hochschild identified emotional labour as a significant contributor to job stress due to its requirements for emotional expression and distortion [2]. Job stress hinders job satisfaction and is influenced by organisational structure, work environment, and emotional labour. Persistent job stress and emotional labour are linked to burnout symptoms [4–6]. Freudenberg was the first to describe burnout as work-related stress [7]. He defined it as a sudden loss of motivation and indifference to patients, caused by job stress, manifesting as physical, emotional, and mental exhaustion. Burnout leads to patient depersonalisation, diminishes employee capabilities, negatively impacts health and organisational commitment and increases turnover intention [8–10].

Most studies on emotional labour, job satisfaction, and burnout in hospitals have focused on nurses. Few have examined emotional labour in medical technicians, along with their job satisfaction and burnout, as a distinct occupational group [11–14]. This study differentiates itself by examining four occupational groups—medical technologists, radiology technologists, physical therapists, and dental hygienists—to determine the relationships among emotional labour, job satisfaction, and burnout in medical technicians and to provide foundational data for human resource management.

2.1 Participants

This study selected 570 medical technologists, radiology technologists, physical therapists, and dental hygienists from six hospitals under the Korea Veterans Health Service: VHS Medical Center, Gwangju Veterans Hospital, Busan Veterans Hospital, Daegu Veterans Hospital, Daejeon Veterans Hospital, and Incheon Veterans Hospital. The required sample size for analysis was calculated to be 280 using G*Power 3.1.9.7. The survey, which included clear response instructions, was administered online via Google Docs due to the Covid-19 pandemic and the geographical distribution of the hospitals. The survey was conducted from July 1 to July 31, 2022, and 290 participants responded, yielding a response rate of 50.9%. This study was approved by the Institutional Review Board (IRB) of the Gwangju Veterans Hospital (IRB approval no.: human 2022-12-6).

2.2 Materials

The survey comprised 102 items: 19 regarding sociodemographic characteristics and subjective health status, 24 on emotional labour, 37 on job satisfaction, and 22 on burnout. Items requiring reverse scoring were appropriately reverse-coded.

2.2.1 Sociodemographic characteristics and subjective health status

Sociodemographic variables included participants' sex, age, marital status, height, weight, exercise habits, hobbies, educational level, religion, affiliated hospital, occupational group, customer service involvement, work pattern, service period, position, wage, commute time, and subjective health status. Age was categorised as “20s”, “30s”, “40s”, “50s”, and “60s and older”. Marital status was designated as “unmarried” or “married”. Height and weight data were used to calculate body mass index (BMI). BMI was categorised as underweight (less than 18.5), normal weight (18.5–23), or overweight (23 or higher) according to the Clinical Practice Guidelines for Obesity 2020 from the Korean Society for the Study of Obesity [15]. Exercise habits and hobbies were classified as “no” or “yes”. Subjective health status was classified as “very poor”, “poor”, “moderate”, “good”, and “very good”. Educational level was classified into “junior college”, “university”, and “graduate school or higher”. Religion was categorised as “no” or “yes”. Affiliated hospitals included “VHS Medical Centre”, “Gwangju Veterans Hospital”, “Busan Veterans Hospital”, “Daegu Veterans Hospital”, “Daejeon Veterans Hospital”, and “Incheon Veterans Hospital”. Occupational groups were categorised as “medical technologist”, “radiology technologist”, “physical therapist”, and “dental hygienist”. Customer service involvement was coded as “no” or “yes”. Work patterns were categorised as “day work” or “other (shift work, night work)”. Service period in the current job was classified as “less than 2 years”, “2–4 years”, “5–9 years”, “10–19 years”, and “20 years or more”. Positions were organised as “staff or lower”, “assistant manager”, “senior staff”, “manager”, and “general manager”. Income was classified into “less than 2 million KRW”, “2 million–less than 3 million KRW”, “3 million–less than 4 million KRW”, “4 million–less than 5 million KRW”, “5 million–less than 6 million KRW”, and “6 million KRW or more”. Commute time was segmented as “less than 30 minutes”, “less than 1 hour”, “less than 1.5 hours”, “less than 2 hours”, and “2 hours or more”. Subjective health status was again sorted as “very poor”, “poor”, “moderate”, “good”, and “very good”.

2.2.2 Factors considered when choosing the current job and while changing jobs

Factors considered when choosing the current job were classified as “pay”, “working environment and welfare”, “company size and growth opportunities”, “education system”, “transportation convenience”, and “other”. Factors considered when changing jobs were classified as “pay”, “working environment and welfare”, “company size and growth opportunities”, “education system”, “transportation convenience”, and “other”.

2.2.3 Emotional labour

Emotional labour was measured using the short version of the Korean Emotional Labor Scale (K-ELS) by Jang [16] after revising and supplementing the items. The scale included 24 items in five domains: emotional demand and regulation (5 items), overload and conflict in customer service (3 items), emotional disharmony and hurt (6), organisational surveillance and monitoring (3), and lack of a supportive system (7). Responses were rated on a 5-point Likert scale (1 = “strongly disagree”; 5 = “strongly agree”). Cronbach’s ⍺ was 0.76.

2.2.4 Job satisfaction

Job satisfaction was measured using the Job Descriptive Index, developed by Smith et al. [17] revised by Lee [18]. The scale comprised 37 items across seven domains: the work itself (5 items), pay (7), supervision (7), promotion (5), coworkers (5), organisation (5), and job change (4). Responses were rated on a 5-point Likert scale (1 = “strongly disagree”; 5 = “strongly agree”). Cronbach’s ⍺ was 0.85.

2.2.5 Burnout

Burnout was measured using the Maslach Burnout Inventory, developed by Maslach and Jackson [19] revised and translated by Choi [20]. The scale included 22 items in three domains: emotional exhaustion (9 items), depersonalisation (5), and reduced personal accomplishment (8). Responses were rated on a 5-point Likert scale (1 = “strongly disagree”; 5 = “strongly agree”). Cronbach’s ⍺ was 0.85.

2.3 Statistical analysis

Stata/MP 14.1 (StataCorp LLC, Texas, USA) was used for analysis. The emotional labour, job satisfaction, and burnout scores were compared using independent samples t-tests and ANOVA. Pearson’s correlation analysis was employed to determine relationships among emotional labour, job satisfaction, and burnout. Multiple regression analysis was used to determine causal relationships, with burnout as the dependent variable and emotional labour and job satisfaction as independent variables.

3.1 Sociodemographic characteristics of the study participants

For sociodemographic characteristics and subjective health status, we found significant differences between occupational groups regarding sex (P < 0.001), BMI (P = 0.001), educational level (P = 0.001), customer service (P < 0.001), work pattern (P < 0.001), service period (P < 0.001), and income (P = 0.001). No significant differences were found for age, marital status, subjective health status, religion, affiliation, position, commute time, or job selection and change factors.

Emotional labour scores (range: 24–120) were highest among dental hygienists at 85.56, followed by radiology technologists at 79.85, medical technologists at 79.79, and physical therapists at 77.94, with significant differences between groups (P = 0.009).

Job satisfaction scores (range: 37–185) were highest among physical therapists at 120.95, followed by radiology technologists at 117.18, medical technologists at 114.98, and dental hygienists at 112.90. These differences were not significant (P = 0.612).

Burnout scores (range: 22–110) were highest among dental hygienists at 66.23, followed by medical technologists at 61.03, radiology technologists at 60.25, and physical therapists at 57.22, showing significant differences between groups (P < 0.001) (Table 1).

[Insert Table 1 here]

3.2 Factors associated with burnout by occupation

Multiple regression analyses were conducted using two models to identify factors affecting burnout by occupational group. In Model I, burnout was the dependent variable, with emotional labour, job satisfaction, and subjective health status as independent variables. In Model II, we considered sociodemographic characteristics and factors influencing job selection or job change (Table 2).

3.2.1 Medical technologists

In Model Ⅰ, emotional labour (B=0.280, P=0.003) and job satisfaction (B=-0.202, P=0.000) significantly affected burnout. In Model II, job satisfaction remained a significant factor (B=-0.203, P=0.014).

3.2.2 Radiology technologists

In Model Ⅰ, emotional labour (B=0.265, P=0.021), job satisfaction (B=-0.228, P=0.001), and subjective health status (B=2.971, P=0.022) significantly affected burnout. In Model II, emotional labour continued to show a significant effect (B=0.546, P=0.024).

3.2.3 Physical therapists

In Model Ⅰ, emotional labour (B=0.361, P<0.001) and subjective health status (B=2.219, P=0.043) had a significant effect on burnout. In Model II, both emotional labour (B=0.466, P<0.001) and subjective health status (B=3.234, P=0.029) remained significant factors.

3.2.4 Dental hygienists

In Model Ⅰ, emotional labour (B=0.337, P=0.010) and subjective health status (B=3.702, P=0.001) significantly affected burnout. In Model II, subjective health status was the significant factor (B=5.788, P=0.030).

[Insert Table 2]

In advanced countries, mental health issues are increasingly addressed holistically rather than as isolated problems in individuals or organisations. These issues can significantly reduce both productivity in companies [16] and the quality of healthcare services. This study examined correlations among emotional labour, job satisfaction, and burnout across four occupational groups of medical technicians, expanding beyond previous studies that focused on a single group.

Our analyses of the differences between these occupational groups with sociodemographic characteristics, subjective health status, factors considered when choosing the current job, factors to consider when changing jobs, emotional labour, job satisfaction, and burnout showed significant differences in sex, which may be due to the overwhelmingly high proportion of female dental hygienists. Educational level also differed significantly. The proportion of highly educated people was the highest in the physical therapists. The highest job satisfaction scores were found among physical therapists, likely due to the increased human capital associated with higher education, which helps mitigate uncertainties related to the constantly changing medical technology and healthcare environment [17]. Customer service involvement varied significantly, across groups, with most radiology technologists, physical therapists, and dental hygienists engaged in customer service, compared to only approximately half of medical technologists. Work patterns also differed, with medical and radiology technologists working day and night shifts, while physical therapists and dental hygienists primarily worked daytime hours. Pay disparities, may result from variations in additional pay due to different work patterns. Emotional labour showed significant differences among the groups.

The Korea Employment Information Service ranked dental hygienists 14th, radiology technologists 95th, hospital administrative staff 27th, occupational therapists 53rd, and nursing assistants 60th in its ‘Top 100 Occupations with High Emotional Labor’, confirming the high emotional labour demands in many medical professions.

Burnout was also examined in relation to sociodemographic characteristics, subjective health status, and job-related factors by occupational group. We found no significant differences in sex across the groups, but the average burnout scores were higher in women than in men, consistent with previous research on physical therapists [21]. Marital status did not significantly affect burnout, although medical technologists and physical therapists showed higher average burnout scores among the unmarried, whereas radiology technologists and dental hygienists had higher scores among the married. Prior studies on medical technologists [13], radiology technologists [22], physical therapists [23], and dental hygienists [24] have concluded that married individuals tend to be more psychologically stable and experience less burnout due to emotional conflict. This study supports these findings for medical technologists and physical therapists but not for radiology technologists and dental hygienists. Among medical technologists, significant differences in burnout were observed based on hospital affiliation, with the highest average burnout score at VHS Medical Center. No significant differences were found for radiology technologists, physical therapists, and dental hygienists. The differences in burnout scores may be linked to job satisfaction’s impact on burnout, as suggested by the regression analysis, with previous research also indicating differences in job satisfaction by hospital size [25].

For customer service, no significant differences were found among the four occupational groups, although those providing direct customer service exhibited higher average burnout scores. Emotional labour experienced during customer interactions may contribute to burnout. Regarding the service period, medical technologists, radiology technologists, and dental hygienists showed significant differences. All four occupational groups recorded the highest average burnout scores within the 5–9-year service period. This finding contradicts previous research [13] that found that shorter service periods correlate with lower personal accomplishment and higher burnout due to unfamiliarity with work. The data suggest that burnout increases until the 5–9-year mark and then declines, indicating the need for systematic career management.

In terms of position, significant differences were observed among medical technologists and physical therapists, with staff and lower positions reporting the highest average burnout scores. This is consistent with previous studies on nurses [26] and medical technologists [13]. Furthermore, higher positions among medical technologists and physical therapists were associated with lower burnout scores, while radiology technologists and dental hygienists showed high burnout scores until reaching assistant manager positions, after which scores declined. This may be due to the similarity in tasks performed by radiology technologists and dental hygienists before attaining senior positions.

Income levels also revealed significant differences in burnout among medical technologists and radiology technologists. The highest average burnout scores were found in medical technologists earning less than 2 million KRW, radiology technologists earning 5–less than 6 million KRW, physical therapists earning less than 2 million KRW, and dental hygienists earning 6 million KRW or more. This is in line with previous research [13], suggesting that burnout is influenced more by confounding variables such as service period and position than by income level.

Subjective health status showed significant differences across all four occupational groups: medical technologists, radiology technologists, physical therapists, and dental hygienists. Participants who perceived a decline in their health reported higher average burnout scores, consistent with previous research on nurses [27], which highlighted differences in burnout based on health status.

Although it is not shown in a table, correlation analysis revealed significant relationships among emotional labour, job satisfaction, and burnout across all occupational groups. Emotional labour and burnout were significantly correlated, with the highest correlation in radiology technologists, followed by physical therapists, dental hygienists, and medical technologists. This finding aligns with previous studies on hospital workers [11, 13, 28], nurses [29, 30], hair designers and hotel employees [31], and call centre workers [32].

Job satisfaction and burnout showed significant negative correlations across all occupational groups, with the strongest correlation in radiology technologists, followed by physical therapists, medical technologists, and dental hygienists. This result is consistent with previous studies on nurses [26, 33] fitness instructors [34] and daycare teachers [35], where higher job satisfaction was linked to lower burnout. Emotional labour and job satisfaction also showed significant negative correlations across all groups, with the strongest correlation in radiology technologists, followed by physical therapists, dental hygienists, and medical technologists.

A multiple regression analysis was conducted in Models I and II to identify factors affecting burnout by occupational group. Among medical technologists, Model I revealed significant correlations between emotional labour, job satisfaction, and burnout, while Model II indicated only job satisfaction significantly correlated with burnout, partially aligning with previous research [13]. The lower proportion of medical technologists engaged in direct customer service may explain why job dissatisfaction was a more significant predictor of burnout than emotional labour. Enhancing job satisfaction through institutional improvements could mitigate burnout in this group.

For radiology technologists, Model I indicated significant correlations among emotional labour, job satisfaction, subjective health status, and burnout. Model II, however, showed a significant correlation only between emotional labour and burnout, consistent with previous findings [14]. These results suggest the need for management programs to regulate emotional labour among radiology technologists.

In physical therapists, both Models I and II demonstrated significant correlations between emotional labour, subjective health status, and burnout, in line with prior studies [21]. Given their direct patient interactions and physical demands, physical therapists are particularly vulnerable to emotional labour, underscoring the need for targeted management and health programs.

For dental hygienists, Model I identified significant correlations between emotional labour, subjective health status, and burnout. However, Model II revealed that only subjective health status significantly correlated with burnout, diverging from earlier research [11]. This suggests that health concerns are a more critical factor in burnout for dental hygienists, highlighting the importance of implementing employee health programs.

As burnout predictors vary by occupational group, targeted interventions addressing specific factors are essential.

This study has some limitations. The response rate was low, yet mandatory responses enabled comprehensive data collection within a short period. The study's focus on medical technicians at Korea Veterans Health Service hospitals limits generalisability. These hospitals, operating under a single public organisation across six major Korean cities, maintain consistent work environments and salary structures, providing controlled conditions [36]. Despite these limitations, this study was the first in Korea to examine differences in burnout, emotional labour, and job satisfaction among medical technicians by occupational group, offering a foundation for future research.

Author Contributions: Conceptualisation: Y-H. S; Formal Analysis: Y-H. S, E-T. H; Funding acquisition: E-T. H; Writing, original draft preparation: Y-H. S, E-T. H; Writing, review and editing: Y-H. S.

Conflict of Interest: None.

Acknowledgments:

Data statement availability: The data that support the findings of this study are openly available in [repository name] at http://doi.org/[doi], reference number [reference number].

Funding statement: This study was supported by a VHS Medical Centre Research Grant (VHSMC 22056), Republic of Korea.

Ethics approval and consent to participate: Informed consent was obtained from all participants in writing prior to their participation in the study.

Consent for publication

Kim YS, Chung EY, Kim JN, Seo YJ. The effect of emotional labor on job stress, burnout and job satisfaction of hospital nurses. Public Health Res. 2014;40:75-88. https://doi.org/10.22900/kphr.2014.40.4.007.
Hochschild AR. The managed heart: Commercialization of human feeling. Berkeley: University of California Press; 1983. https://doi.org/10.1525/9780520951853.
Korea Employment Information Service [Internet]. Comparative analysis of jobs that require emotional labor. 2015. https://www.keis.or.kr/user/bbs/main/137/3963/bbsDataView/27976.do. Accessed 2024 July 10.
Lee MH. Relationship of job-satisfaction due to radiographer's job-characteristics and job-stress. J Korea Contents Assoc. 2015;15:408-15. https://doi.org/10.5392/JKCA.2015.15.08.408.
Shin HS. Effect of emotional labor on burnout and job satisfaction. J Korea Contents Assoc. 2012;12:415-24. https://doi.org/10.5392/JKCA.2012.12.07.415.
Lee JY, Jee JH. Moderating effects of emotional intelligence on the relationships between job demand and burn-out: Focused on tourism employees; 2007. J Hum Resour Manag [Internet]. http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE08809509.
Freudenberger HJ. Staff burn‐out. J Soc Issues. 1974;30:159-65. https://doi.org/10.1111/j.1540-4560.1974.tb00706.x.
Morris JA, Feldman DC. The dimensions, antecedents, and consequences of emotional labor. Acad Manag Rev. 1996;21:986-1010. https://doi.org/10.5465/amr.1996.9704071861.
Kim PY, Song SI. Burnout and coping strategies among tourism service employees; 2008. IJOC [Internet]. http://lod.nl.go.kr/resource/KSI000829262. Accessed 2024 July 10.
Jackson SE, Turner JA, Brief AP. Correlates of burnout among public service lawyers. J Organ Behav. 1987;8:339-49. https://doi.org/10.1002/job.4030080406.
Kang MH, Jang SJ. Convergent relationship between emotional labor, burnout, Job satisfaction and turnover intention in the dental hygienists; 2017. J Korean Converg Soc [Internet]. https://www.earticle.net/Article/A307024. Accessed 2024 July 10.
Yoon HJ, Moon KR. Moderating effect of self-efficacy and social support in the relationship between physical therapist’s job stress and psychological burnout. J Korea Soc Comput Inform. 2020;25:137-44. https://doi.org/10.9708/jksci.2020.25.08.137.
Song YB, Lee TY, Kim YR. Relationships of emotional labor, job satisfaction and burnout among medical technologists in a university hospital. J Korea Acad Ind Coop Soc. 2014;15:5140-9. https://doi.org/10.5762/KAIS.2014.15.8.5140.
Park JH. Effect of radiographer emotional labor on turnover intention and job satisfaction. J Environ Health Sci. 2018;44:591-8. https://doi.org/10.5668/JEHS.2018.44.6.591.
Korean Society for the Study of Obesity [Internet]. Clinical practice quick reference guidelines for obesity 2020. 2022. http://general.kosso.or.kr/html/?pmode=BBBS0001300003&page=1&smode=view&seq=1375&searchValue=&searchTitle=strTitle. Accessed 2024 July 10.
Jang SJ. Development of questionnaire items for Korean emotional labor assessment tool. Korean J Occup Environ Med. 2013.
Smith PC. The measurement of satisfaction in work and retirement: A strategy for the study of attitudes; 1969.
Lee SJ. A study on job satisfaction of social workers; 1994. https://www.riss.kr/link?id=T921775.
Maslach C, Jackson SE. The measurement of experienced burnout. J Organ Behav. 1981;2:99-113.
Choi HY, Chung NW. Perfectionism tendency, social support, and burnout among counselors. Korean J Health Psychol. 2003;8:279-300.
Hur YJ, Lee SM. Factors affecting emotional labor among physical therapists and occupational therapists. J Korea Converg Soc. 2019;10:237-47. https://doi.org/10.15207/JKCS.2019.10.9.237.
Hwang SK, Jeong BJ. A study on the impact of professional burnout and job autonomy on turnover intention in radiologic technologists. J Korean Soc Radiol. 2011;5:267-75. https://doi.org/10.7742/jksr.2011.5.5.267.
Noh HK, Park HJ. A study on the burnout of physical therapists and its influencing factors. KCPMD. 2008;51:117-45. https://doi.org/10.20971/kcpmd.2008.51.1.117.
Han YK, Kim HH. The relationship among burnout, compassion fatigue, compassion satisfaction and social support of clinical dental hygienists. JDHC. 2014;14:256-63. https://kiss.kstudy.com/Detail/Ar?key=3249871.
Lee MH, Kim JK. A comparative study on nursing practice environment, professionalism, and job satisfaction according to hospital size. J Korean Acad Nurs Adm. 2013;19:470. https://doi.org/10.11111/jkana.2013.19.4.470.
Chung BY, Han JY. Influential factors related to job satisfaction in hospice nurses: Focus on emotional labor and burnout. J Korean Acad Nurs Adm. 2014;20:322. https://doi.org/10.11111/jkana.2014.20.3.322.
Kim SH, Yang YS. A meta analysis of variables related to burnout of nurse in Korea. J Digit Converg. 2015;13:387-400. https://doi.org/10.14400/JDC.2015.13.8.387.
Moon S. An effect of hospital employee’s emotional labor on burnout-focusing on emotional competence of supervisor and personal moderating effect of emotional intelligence. Inje University; 2011.
Baik DW, Yom YH. Effects of social support and emotional intelligence in the relationship between emotional labor and burnout among clinical nurses. J Korean Acad Nurs Adm. 2012;18:271-80. https://doi.org/10.11111/jkana.2012.18.3.271.
Yang YK. A study on burnout, emotional labor, and self-efficacy in nurses. J Korean Acad Nurs Adm. 2011;17:423-31. https://doi.org/10.11111/jkana.2011.17.4.423.
Park YB, Ahn DH, Lee SW. Influence of emotion labor on job stress and burnout for the hotel employee. J Korea Contents Assoc. 2009;9:853-64. https://doi.org/10.5392/JKCA.2009.9.12.853.
Yoon SY. A study on the emotional labor, burnout and turnover intention of airline call center wokers; 2010. J Adv Navig Tech [Internet]. https://www.researchgate.net/publication/264139898_A_Study_on_the_Emotional_Labor_Burnout_and_Turnover_Intention_of_Airline_Call_Center_Wokers. Accessed 2024 July 10.
Jang TU, Choi EJ. Relationships between occupational stress, burnout and job satisfaction of physician assistants. J Korean Public Health Nurs. 2016;30:122-35. https://doi.org/10.5932/JKPHN.2016.30.1.122.
Cho MS, Yi ES. The relationship between emotional labor, job burnout and job satisfaction in fitness center instructors. J Digit Converg. 2017;15:611-20. https://doi.org/10.14400/JDC.2017.15.11.611.
Kim SY. The influence of career, burnout, and play-teaching efficacy on the job satisfaction of childcare teachers. Korean J Childcare Educ. 2015;11:481-99. https://doi.org/10.14698/JKCCE.2015.11.481.
National Medical Center [Internet]. 2020 status of public medical institutions. 2021. https://www.ppm.or.kr/board/thumbnailList.do. Accessed 2024 July 10

Table 1. Sociodemographic characteristics of the study’s participants and burnout levels across variables by occupation(N=290)

Variables	Medical technologists(N=89)			Radiological technologists(N=67)			Physical therapists(N=82)			Dental hygienists(N=52)			N	P
Variables	N (%)	M±SD	P	N (%)	M±SD	P	N (%)	M±SD	P	N (%)	M±SD	P
Sex			0.071			0.354			0.064			0.261		<0.001
Male	36(40.4)	58.39±9.48		51(76.1)	59.47±12.28		57(69.5)	55.70±11.69		3(5.8)	59.00±7.00		147
Female	53(59.6)	62.83±12.33		16(23.9)	62.75±12.22		25(30.5)	60.68±9.33		49(94.2)	66.67±11.50		143
Age (year)			0.001			0.544			0.099			0.424		0.212
20–29	16(18.0)	59.38±11.95		12(17.9)	57.92±15.83		18(22.0)	60.83±8.35		10(19.2)	63.10±17.12		56
30–39	38(42.7)	66.08±10.25		29(43.3)	62.31±13.04		38(46.3)	57.45±11.04		25(48.1)	68.52±9.79		130
40–49	17(19.1)	59.00±9.82		11(16.4)	56.73±7.46		18(22.0)	56.78±14.55		15(28.9)	65.60±8.71		61
50–60	18(20.2)	53.78±10.47		15(22.4)	60.73±10.38		8(9.7)	49.00±3.82		2(3.8)	58.00±15.56		43
Marital status			0.147			0.483			0.512			0.791		0.671
Unmarried	44(49.4)	63.43±12.15		26(38.8)	58.92±14.20		36(43.9)	58.81±9.62		21(40.4)	65.71±13.26		127
Married	45(50.6)	58.68±10.36		41(61.2)	61.10±10.94		46(56.1)	55.91±12.40		31(59.6)	66.58±10.14		163
BMI			0.315			0.434			0.469			0.878		0.001
Underweight (<18.5)	4(4.5)	58.00±6.98		6(9.0)	65.83±15.64		5(6.1)	63.20±8.53		5(9.6)	68.40±22.78
Normal (18.5-22.9)	47(52.8)	62.77±10.59		19(28.3)	61.05±7.63		33(40.2)	56.61±10.71		29(55.8)	66.34±10.39
Overweight (23≤)	38(42.7)	59.21±12.59		42(62.7)	59.10±13.42		44(53.7))	57.00±11.83		18(34.6)	65.44±9.33
Hobbies and exercise			0.674			0.730			0.768			0.274		0.069
No	32(36.0)	61.72±11.10		16(23.9)	61.19±12.91		17(20.7)	57.94±11.73		19(36.5)	68.53±9.40		84
Yes	57(64.0)	60.65±11.67		51(76.1)	59.96±12.16		65(79.3)	57.03±11.15		33(63.5)	64.91±12.33		206
Educational level			0.264			0.645			0.742			0.874		0.001
Junior college	26(29.2)	60.85±10.35		23(34.3)	62.04±10.53		11(13.4)	56.18±12.74		20(38.5)	65.20±12.44		80
University	52(58.4)	62.19±12.39		41(61.2)	59.51±13.04		56(68.3)	56.89±11.19		28(53.8)	66.79±11.34		177
Graduate school	11(12.4)	56.00±7.78		3(4.5)	56.67±16.26		15(18.3)	59.20±10.65		4(7.7)	67.50±7.33		33
Religion			0.027			0.644			0.306			0.078		0.743
No	30(33.7)	57.30±12.80		28(41.8)	59.43±10.48		32(39.0)	58.81±12.32		21(40.4)	62.86±11.64		111
Yes	59(66.3)	62.93±10.24		39(58.2)	60.85±13.49		50(61.0)	56.20±10.44		31(59.6)	68.52±10.80		179
Affiliated veterans hospital			0.000			0.158			0.177			0.170		0.598
Central	26(29.3)	69.42±7.89		24(35.8)	62.21±11.40		15(18.3)	62.73±9.76		18(34.6)	67.00±9.68		83
Gwangju	18(20.2)	56.28±13.61		14(20.9)	64.57±14.80		29(35.4)	56.24±12.00		9(17.3)	68.33±11.05		70
Busan	14(15.7)	55.00±10.55		10(14.9)	51.60±11.13		13(15.9)	52.31±13.48		8(15.4)	71.00±4.00		45
Daegu	16(18.0)	56.69±9.64		8(11.9)	61.13±10.78		6(7.3)	54.17±8.57		7(13.5)	58.14±16.80		37
Daejeon	10(11.2)	63.80±7.52		4(6.0)	56.50±7.19		6(7.3)	61.33±9.56		8(15.4)	62.13±13.10		28
Incheon	5(5.6)	59.80±8.41		7(10.5)	58.43±11.54		13(15.8)	57.46±8.40		2(3.8)	75.50±6.36		27
Customer service			0.089			0.399			.			0.361		<0.001
No	45(50.6)	59.00±9.65		2(3.0)	53.00±19.80		1(1.2)	44.00±0		3(5.8)	60.33±9.29		51
Yes	44(49.4)	63.11±12.75		65(97.0)	60.48±12.12		81(98.8)	57.38±11.18		49(94.2)	66.59±11.48		239
Work pattern			0.954			0.645			.			.		<0.001
Day work	64(71.9)	61.08±11.25		51(76.1)	59.86±11.23		82(100.0)	57.22±11.21		51(98.1)	66.24±11.50		248
Day and night shifts	25(28.1)	60.92±12.08		16(23.9)	61.50±15.43		0			1(1.9)	66.00±0		42
Service period (year)			0.021			0.018			0.214			0.001		<0.001
<2	8(9.0)	62.13±17.83		7(10.5)	46.14±16.30		33(40.2)	58.76±9.53		7(!3.5)	52.43±12.16		55
2–4	16(18.0)	60.75±8.74		20(29.9)	63.25±9.95		15(18.3)	55.33±11.82		12(23.1)	71.00±6.97		63
5–9	24(27.0)	66.04±10.36		9(13.4)	63.89±13.19		12(14.6)	60.50±14.96		8(15.4)	74.00±10.11		53
10–19	14(15.7)	62.71±8.15		15(22.3)	61.33±10.74		13(15.9)	57.46±12.84		19(36.5)	65.05±9.69		61
20≤	27(30.3)	55.56±11.27		16(23.9)	59.63±10.67		9(11.0)	50.00±4.21		6(11.5)	66.17±11.57		58
Position			0.008			0.743			0.030			0.255		0.087
Staff	30(33.7)	66.07±11.61		24(35.8)	59.33±14.63		45(54.9)	60.42±9.79		25(48.1)	64.88±11.24		124
Assistant manager	40(44.9)	60.13±9.68		26(38.7)	61.96±10.27		23(28.0)	55.26±14.01		20(38.5)	67.25±11.82		109
Senior staff	9(10.1)	58.78±12.95		7(10.5)	62.29±14.15		5(6.1)	51.60±7.80		4(7.7)	75.00±6.98		25
Manager	7(7.9)	51.43±11.73		6(9.0)	58.33±9.14		6(7.3)	50.83±3.37		3(5.8)	59.00±11.14		16
General manager	3(3.4)	52.00±2.00		4(6.0)	54.00±11.94		3(3.7)	46.33±3.79		0			10
Pay (￦10.000/Month)			0.002			0.025			0.091			0.907		0.001
〈200	2(2.3)	81.50±9.19		2(3.0)	33.50±10.61		1(1.2)	66.00±0		2(3.8)	65.00±1.41		7
200–299	10(11.2)	57.50±13.30		10(14.9)	62.10±7.61		38(46.3)	58.66±9.06		17(32.7)	65.29±13.09		75
300–399	44(49.4)	64.43±9.62		34(50.6)	61.15±12.48		25(30.5)	59.00±14.36		25(48.1)	67.68±11.02		128
400–499	26(29.2)	56.04±11.02		11(16.5)	60.27±11.46		11(13.4)	54.27±8.64		7(13.5)	63.29±11.64		55
500–599	3(3.4)	54.33±9.29		7(10.5)	64.57±11.01		4(4.9)	44.75±7.89		0			14
600≤	4(4.5)	59.75±10.72		3(4.5)	51.67±11.15		3(3.7)	48.67±6.51		1(1.9)	69.00±0		11
Subjective health status			0.005			<0.001			<0.001			<0.001		0.142
Very poor	4(4.5)	46.75±17.65		3(4.5)	33.33±12.70		4(4.9)	37.25±15.46		3(5.8)	44.33±14.19		14
Poor	16(18.0)	54.75±11.64		14(20.9)	53.93±8.32		18(22.0)	51.67±8.20		9(17.3)	60.33±11.24		57
Moderate	35(39.3)	62.74±8.36		28(41.8)	61.64±8.58		38(46.3)	58.08±9.29		14(26.9)	65.36±8.71		115
Good	26(29.2)	63.62±9.75		20(29.8)	65.25±11.88		16(19.5)	64.94±9.19		16(30.8)	69.00±8.33		78
Very good	8(9.0)	64.88±16.49		2(3.0)	75.50±16.26		6(7.3)	61.17±11.25		10(19.2)	74.90±7.71		26
Commute time			0.937			0.303			0.966			0.414		0.201
〈30 min	48(53.9)	60.40±13.46		37(55.2)	61.95±12.77		49(59.8)	57.00±12.07		34(65.4)	64.97±12.74		168
〈1 hour	27(30.3)	61.41±8.88		24(35.8)	56.75±11.07		23(28.0)	57.35±9.93		16(30.8)	67.88±8.08		90
〈1 hour 30 min	12(13.5)	62.67±8.77		2(3.0)	56.00±21.21		10(12.2)	58.00±10.55		2(3.8)	74.50±6.36		26
〈2 hours	0			2(3.0)	63.50±0.71		0			0			2
2 hours≤	2(2.3)	61.50±0.71		2(3.0)	72.00±5.66		0			0			4
Criteria for job selection			0.076			0.796			0.621			0.770		0.115
Pay	26(29.2)	63.81±9.91		12(17.9)	59.50±13.59		14(17.1)	55.64±15.15		17(32.7)	67.24±13.05		69
Work environment and welfare	44(49.4)	59.14±11.59		40(59.7)	61.00±10.06		61(74.3)	57.00±10.11		32(61.5)	65.97±10.84		177
Company size and growth opportunities	11(12.4)	56.45±13.46		8(11.9)	58.13±17.27		4(4.9)	61.25±16.15		1(1.9)	52.00±0		24
Education system	0			0			0			0			0
Transportation convenience	2(2.3)	71.50±4.95		4(6.0)	64.25±18.79		0			1(1.9)	71.00±0		7
Other	6(6.7)	67.83±7.99		3(4.5)	53.67±16.17		3(3.7)	63.67±5.03		1(1.9)	67.00±0		13
Criteria for a job change			0.004			0.709			0.228			0.663		0.358
Pay	39(43.8)	64.56±10.05		30(44.8)	61.43±10.89		37(45.1)	56.65±12.29		13(25.0)	67.77±12.78		119
Work environment and welfare	41(46.1)	59.56±11.37		28(41.8)	59.93±11.63		39(47.6)	59.03±10.00		35(67.3)	65.09±11.24		143
Company size and growth opportunities	6(6.7)	54.67±6.89		7(10.4)	55.71±20.34		5(6.1)	49.20±9.88		2(3.8)	77.00±5.66		20
Education system	1(1.1)	68.00±0		0			0			0			1
Transportation convenience	2(2.3)	38.00±16.97		1(1.5)	55.00±0		0			1(1.9)	68.00±0		4
Other	0			1(1.5)	71.00±0		1(1.2)	48.00±0		1(1.9)	63.00±0		3
Emotional labour		79.79±11.86			79.85±13.01			77.94±14.07			85.56±11.73		290	0.009
Job satisfaction		114.98±22.55			117.18±21.74			120.95±19.61			112.90±22.14		290	0.146
Burnout		61.03±11.42			60.25±12.25			57.22±11.21			66.23±11.39		290	<0.001

Data are presented as mean ±SD or number (%).

P-values were calculated by t-test or ANOVA (analysis of variance).

Abbreviations: BMI, body mass index.

Table 2. Factors associated with burnout by occupation

Variables		Model Ⅰ				Model Ⅱ
Variables		B	t	P	R²	B	t	P	R²
Medical technologist (N=89)	Emotional labour	0.280	3.070	0.003		0.203	1.810	0.077
	Job satisfaction	-0.202	-4.200	0.000		-0.203	-2.580	0.014
	Subjective health status	1.342	1.290	0.202		0.500	0.320	0.754
					0.390				0.483
Radiological technologist (N=67)	Emotional labour	0.265	2.370	0.021		0.546	2.470	0.024
	Job satisfaction	-0.228	-3.390	0.001		-0.311	-2.100	0.050
	Subjective health status	2.971	2.340	0.022		1.054	0.380	0.711
					0.590				0.629
Physical therapist (N=82)	Emotional labour	0.361	3.970	0.000		0.466	4.500	0.000
	Job satisfaction	-0.116	-1.940	0.056		-0.011	-0.150	0.881
	Subjective health status	2.219	2.060	0.043		3.234	2.270	0.029
					0.501				0.618
Dental hygienist (N=52)	Emotional labour	0.337	2.690	0.010		0.581	1.460	0.176
	Job satisfaction	-0.098	-1.580	0.120		0.033	0.130	0.897
	Subjective health status	3.702	3.420	0.001		5.788	2.520	0.030
					0.514				0.440
P-values were calculated by multiple logistic regression adjusting for sex, age, marital status, BMI, hobbies & exercise, educational level, religion, customer service, work pattern, service period, position, pay, commute time, factors to consider when choosing a job, factors to consider when changing a job.

No competing interests reported.

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Emotional Labour, Job Satisfaction, and Burnout in Medical Technicians: Insights from the Korea Veterans Health Service

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Abstract

Background

Methods

Results

Conclusion

1. Background

2. Materials and methods

2.1 Participants

2.2 Materials

2.2.1 Sociodemographic characteristics and subjective health status

2.2.2 Factors considered when choosing the current job and while changing jobs

2.2.3 Emotional labour

2.2.4 Job satisfaction

2.2.5 Burnout

2.3 Statistical analysis

3. Results

3.1 Sociodemographic characteristics of the study participants

3.2 Factors associated with burnout by occupation

3.2.1 Medical technologists

3.2.2 Radiology technologists

3.2.3 Physical therapists

3.2.4 Dental hygienists

4. Discussion

Declarations

References

Tables

Additional Declarations

Status:

Version 1