Skip Navigation
Skip to contents

RCPHN : Research in Community and Public Health Nursing

OPEN ACCESS
SEARCH
Search
Advanced Search
mobile menu button

Articles

Page Path
HOME > J Korean Acad Community Health Nurs > Volume 27(1); 2016 > Article
Original Article
The Effect of Depression and Smartphone Dependency on Female College Students' Career Decision-making Self Efficacy
Hee Jung Choi, Jang Hak Yoo
Journal of Korean Academy of Community Health Nursing 2016;27(1):43-50.
DOI: https://doi.org/10.12799/jkachn.2016.27.1.43
Published online: March 31, 2016

1Department of Nursing, Mokpo Catholic University, Mokpo, Korea.

2Department of Nursing, Suwon Women's University, Suwon, Korea.

• Received: January 25, 2016   • Revised: March 16, 2016   • Accepted: March 22, 2016

© 2016 Korean Academy of Community Health Nursing

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

prev next
  • 1,774 Views
  • 6 Download
Full Article

Download PDF Download PDF

  • Purpose
    The purpose of this study was to investigate the relationship of depression and smartphone dependency with female college students' career decision-making self efficacy.
  • Methods
    This was a descriptive study. The survey participants were 497 female college students in M City and S City. Data were collected from November 16 to December 4, 2015 using self-report questionnaires including BDI (Beck Depression Inventory), Smartphone Dependency Scale, and CDMSES-SF (Career Decision-Making Self Efficacy Scale-Short Form). Data were analyzed through descriptive statistics, independent-samples t-test, ANOVA, and stepwise multiple regression.
  • Results
    Career decision-making self efficacy showed significant differences according to religion. Smartphone dependency was found to have a statistically significant negative correlation with career decision-making self efficacy and a positive correlation with depression. Depression was found to have a statistically significant negative correlation with career decision-making self efficacy. Stepwise multiple regression analysis revealed that the predictors of career decision-making self efficacy were depression (7.1%), religion (1.8%), and smartphone dependency (1.3%), accounting for a total of 10.6% of the variance.
  • Conclusion
    This study suggests that interventions to promote female college students' career decision-making self efficacy should consider their depression, religion, and smartphone dependency.
  • Key Words: Depression; Smartphone; Self efficacy
Table 1

General Characteristics of the Subjects (N=497)

Variables Categories n (%) or M±SD
Age 21.58±0.05
Religion Have 241 (48.5)
None 256 (51.5)
Education level of parents ≤Highschool (both) 187 (37.6)
≥College (either of) 310 (62.4)
Number of family members ≤3 129 (26.0)
≥4 368 (74.0)
Income level Lower class 68 (13.7)
≥Middle class 429 (86.3)
Smartphone dependency General user 396 (79.7)
Potential risk user 68 (13.7)
High risk user 33 (6.6)
Depression 9.79±0.39
Career decision-making self-efficacy 84.31±0.52
Table 2

Difference of Career Decision-making Self-efficacy, Depression, Smartphone Dependency according to Subject Characteristics

Variables Categories Depression Smartphone dependency Career decision-making self-efficacy
M±SD F/t/r p M±SD F/t/r p M±SD F/t/r p
Age 0.00 .995 -0.13 .005 0.10 .029
Religion Have 10.45±0.63 -1.61 .108 34.17±0.44 -0.63 .527 85.72±0.72 -2.67 .008
None 9.18±0.47 33.78±0.42 82.98±0.73
Education level of parents ≤Highschool(both) 9.83±0.60 -0.07 .946 33.25±0.49 1.85 .064 84.11±0.85 0.30 .765
≥College(either of) 9.77±0.51 34.40±0.38 84.43±0.65
Number of family members ≤2 9.81±0.85 0.02 .986 33.29±0.63 -1.33 .184 84.43±1.05 0.15 .883
≥3 9.79±0.44 34.21±0.35 84.26±0.59
Income level Lower class 11.53±0.99 -1.77 .077 33.53±0.94 0.58 .565 84.57±1.29 -0.21 .837
≥Middle class 9.52±0.42 34.04±0.32 84.26±0.56
Table 3

The Correlations among Depression, Smartphone Dependency, Career Decision-making Self-efficacy in the Subjects

Variables Career decision-making self-efficacy Depression
Total Planning Goal selection Problem solving Self-appraisal Occupational information
r (p) r (p) r (p) r (p) r (p) r (p)
Smartphone dependency -.20
(<.001)		 -.16
(<.001)		 -.20
(<.001)		 -.15
(<.01)		 -.20
(<.001)		 -.13
(<.01)		 .24
(<.001)
Disturbance of adaptive functions -.17
(<.001)		 -.15
(<.01)		 -.17
(<.001)		 -.11
(<.05)		 -.19
(<.001)		 -.10
(<.05)		 .22
(<.001)
Virtual life orientation -.17
(<.001)		 -.11
(<.05)		 -.16
(<.001)		 -.15
(<.01)		 -.20
(<.001)		 -.11
(<.05)		 .20
(<.001)
Withdrawal -.16
(<.001)		 -.11
(<.05)		 -.18
(<.001)		 -.14
(<.01)		 -.16
(<.001)		 -.11
(<.05)		 .18
(<.001)
Tolerance -.14
(<.01)		 -.15
(<.01)		 -.13
(<.01)		 -.10
(<.05)		 -.13
(<.01)		 -.10
(<.05)		 .16
(<.001)
Depression -.27
(<.001)		 -.27
(<.001)		 -.22
(<.001)		 -.23
(<.001)		 -.23
(<.001)		 -.21
(<.001)
Table 4

Influencing Factors on Career Decision-making Self-efficacy

Variables Career decision-making self-efficacy
B SE β t p R2 Adj. R2
Depression -0.34 .06 -.26 -5.96 <.001 .07 .07
Religion 3.31 .98 .14 3.36 .001 .02 .02
Smartphone dependency -0.24 .07 -.14 -3.22 .001 .01 .01
F=19.68

This study was financially supported by the research fund of Mokpo Catholic University.

  • 1. Ministry of Education, Science, and Technology. Statistical yearbook for employment of higher education graduates [Internet] Seoul: Ministry of Education, Science, and Technology; 2014;cited 2015 November 2. Available from: http://kess.kedi.re.kr/eng/publ/view?survSeq=2014&publSeq=17&menuSeq=3645&itemCode=02&language=en
  • 2. Lee YM, Lee SY. An analysis of female college graduates' school-to-work transition preparation and their performance in the labor market. J Educ Res. 2012;10(2):143–162.
  • 3. Lee YM, Lee SY. The effects of science and technology-majored college women graduates' job preparation behavior on types of their employment status and workplaces. J Lifelong Educ HRD. 2010;6(2):77–98.
  • 4. Shin SM, Jung KA, Koo JH. Survey on women students' perception of employment. Research Report. Seoul: Korean Women's Development Institute; 2008. 12;Report No.: 2008 Research Report-08.
  • 5. Lee EK. A study on the effect of self-efficacy upon the career development [dissertation]. [Seoul]: Ewha Women's University; 2001. 134 p.
  • 6. Park MJ, Kim JH, Jung MS. A qualitative study on the stress of university students for preparing employment. Korean J Couns. 2009;10(1):417–435. Article
  • 7. Hwang KH, Yoo YS, Cho OH. Smartphone overuse and upper extremity pain, anxiety, depression, and interpersonal relationships among college students. J Korea Contents Assoc. 2012;12(10):365–375.Article
  • 8. Kim MK. A study on job preparing stress and depression influencing on smartphone addiction in university students: Focusing on moderating effect of self-control. J Korea Inst Youth Facil Environ. 2014;12(2):117–126.
  • 9. Koh YJ, Lee HI. A study on the changes of behavior patterns in smartphone users by shadowing early iPhone users. J Commod Sci Tech. 2010;28(1):111–120. Article
  • 10. Hwang HS, Sohn SH, Choi YJ. Exploring factors affecting smartphone addiction-characteristics of users and functional attributes. Korea J Broadcast Telecommun Stud. 2011;25(2):277–313.
  • 11. Choi HJ, Yoo JH. A study on the relationship between self-esteem, social support, smartphone dependency, internet game dependency of college students. J East West Nurs Res. 2015;21(1):78–84.Article
  • 12. Lee JC. Theoretical study and comparison analysis of research results on the career decision self-efficacy of university students. J Employ Career. 2013;3(1):49–66.
  • 13. Youn JH. The study on the relationship between self-esteem, career decision self efficacy, employability of college students. J Employ Career. 2015;5(2):29–46. Article
  • 14. Esters LT, Retallick MS. Effect of an experiential and workbased learning program on vocational identity, career decision self-efficacy, and career maturity. Career Tech Educ Res. 2013;38(1):69–83.Article
  • 15. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;127(4):561–571.Article
  • 16. Lee YH, Song JY. A Study of the Reliability and the Validity of the BDI, SDS, and MMPI-D Scales. Korean J Clin Psychol. 1991;10(1):98–113.
  • 17. Shin KW, Kim DI, Jeung YJ, Lee JY, Lee YH, Kim MC, et al. Development of korean smartphone addiction proneness scale for youth and adults. Research Report. Seoul: National Informational Society Agency; 2011. 11;Report No.: NIA IV-RER-11051.
  • 18. Betz NE, Klein KL, Taylor KM. Evaluation of a short form of the career decision-making self-efficacy scale. J Career Assess. 1996;4(1):47–57.ArticlePDF
  • 19. Kwon MS, Nam GW, Seo BK. A survey on internet addiction in 2014. Research Report. Seoul: Ministry of Science, Information, Communication, Technology and Future Planning & National Information Society Agency; 2015. 04;Report No.: NIA VRER-14112.
  • 20. Kim EJ, Choi KW. The effects of the depression and job-seeking stress on the career maturity of university students. J Korean Soc Living Environ Syst. 2012;19(2):231–239.
  • 21. Lin HS, Probst JC, Hsu YC. Depression among female psychiatric nurses in southern Taiwan: Main and moderating effects of job stress, coping behaviour and social support. J Clin Nurs. 2010;19(15-16):2342–2354.ArticlePubMed
  • 22. Suh KH, Chon KK. Spiritual well-being, life stress, and coping. Korean J Health Psychol. 2004;9(2):333–350.
  • 23. Kang ES, Song YS, Cho HS, Kang SN. Relationship of suicidal ideation; depression; spiritual well-being of adolescence. J Korean Acad Psychiatr Ment Health Nurs. 2004;13(2):190–199. ArticlePDF
  • 24. Koh YJ, Lee HI. A study on the changes of behavior patterns in smartphone users by shadowing early iPhone users. J Commod Sci Tech. 2010;28(1):111–120. Article
  • 25. Kim DI, Chung YJ, Lee EA, Kim DM, Cho YM. Development of internet addiction proneness scale-short form (KS scale). Korean J Couns. 2008;9(4):1703–1722.Article
  • 26. Shin JO. Inter-relationships among career barriers, social supports, career decision making self efficacy, and career decision in college students. J Educ Res. 2011;19(2):109–141.
  • 27. Hwang KH, Yoo YS, Cho OH. Smartphone overuse and upper extremity pain, anxiety, depression, and interpersonal relationships among college students. J Korea Contents Assoc. 2012;12(10):365–375.Article
  • 28. Oh CS, Lee BH. Factors influencing career decision making in adolescents and female college students. Korean J Youth Stud. 2005;12(3):93–113.
  • 29. Han NR, Jeon JH. A research on self-efficacy of career decision-making: A case of female college students majoring in office administration. J Secretarial Stud. 2012;21(2):83–99.
  • 30. Jung MY, Lee HR. A structural analysis of anticipated career barriers, career myths, career decision making self-efficacy, and career development of university students. Korean J Couns. 2007;8(1):247–264. Article

Figure & Data

References

    Citations

    Citations to this article as recorded by  

      • PubReader PubReader
      • Cite
        CITE
        export Copy
        Close
      • XML DownloadXML Download
      We recommend
      The Effect of Depression and Smartphone Dependency on Female College Students' Career Decision-making Self Efficacy
      The Effect of Depression and Smartphone Dependency on Female College Students' Career Decision-making Self Efficacy
      Variables Categories n (%) or M±SD
      Age 21.58±0.05
      Religion Have 241 (48.5)
      None 256 (51.5)
      Education level of parents ≤Highschool (both) 187 (37.6)
      ≥College (either of) 310 (62.4)
      Number of family members ≤3 129 (26.0)
      ≥4 368 (74.0)
      Income level Lower class 68 (13.7)
      ≥Middle class 429 (86.3)
      Smartphone dependency General user 396 (79.7)
      Potential risk user 68 (13.7)
      High risk user 33 (6.6)
      Depression 9.79±0.39
      Career decision-making self-efficacy 84.31±0.52
      Variables Categories Depression Smartphone dependency Career decision-making self-efficacy
      M±SD F/t/r p M±SD F/t/r p M±SD F/t/r p
      Age 0.00 .995 -0.13 .005 0.10 .029
      Religion Have 10.45±0.63 -1.61 .108 34.17±0.44 -0.63 .527 85.72±0.72 -2.67 .008
      None 9.18±0.47 33.78±0.42 82.98±0.73
      Education level of parents ≤Highschool(both) 9.83±0.60 -0.07 .946 33.25±0.49 1.85 .064 84.11±0.85 0.30 .765
      ≥College(either of) 9.77±0.51 34.40±0.38 84.43±0.65
      Number of family members ≤2 9.81±0.85 0.02 .986 33.29±0.63 -1.33 .184 84.43±1.05 0.15 .883
      ≥3 9.79±0.44 34.21±0.35 84.26±0.59
      Income level Lower class 11.53±0.99 -1.77 .077 33.53±0.94 0.58 .565 84.57±1.29 -0.21 .837
      ≥Middle class 9.52±0.42 34.04±0.32 84.26±0.56
      Variables Career decision-making self-efficacy Depression
      Total Planning Goal selection Problem solving Self-appraisal Occupational information
      r (p) r (p) r (p) r (p) r (p) r (p)
      Smartphone dependency -.20
      (<.001)      		 -.16
      (<.001)      		 -.20
      (<.001)      		 -.15
      (<.01)      		 -.20
      (<.001)      		 -.13
      (<.01)      		 .24
      (<.001)
      Disturbance of adaptive functions -.17
      (<.001)      		 -.15
      (<.01)      		 -.17
      (<.001)      		 -.11
      (<.05)      		 -.19
      (<.001)      		 -.10
      (<.05)      		 .22
      (<.001)
      Virtual life orientation -.17
      (<.001)      		 -.11
      (<.05)      		 -.16
      (<.001)      		 -.15
      (<.01)      		 -.20
      (<.001)      		 -.11
      (<.05)      		 .20
      (<.001)
      Withdrawal -.16
      (<.001)      		 -.11
      (<.05)      		 -.18
      (<.001)      		 -.14
      (<.01)      		 -.16
      (<.001)      		 -.11
      (<.05)      		 .18
      (<.001)
      Tolerance -.14
      (<.01)      		 -.15
      (<.01)      		 -.13
      (<.01)      		 -.10
      (<.05)      		 -.13
      (<.01)      		 -.10
      (<.05)      		 .16
      (<.001)
      Depression -.27
      (<.001)      		 -.27
      (<.001)      		 -.22
      (<.001)      		 -.23
      (<.001)      		 -.23
      (<.001)      		 -.21
      (<.001)
      Variables Career decision-making self-efficacy
      B SE β t p R2 Adj. R2
      Depression -0.34 .06 -.26 -5.96 <.001 .07 .07
      Religion 3.31 .98 .14 3.36 .001 .02 .02
      Smartphone dependency -0.24 .07 -.14 -3.22 .001 .01 .01
      F=19.68
      Table 1 General Characteristics of the Subjects (N=497)

      Table 2 Difference of Career Decision-making Self-efficacy, Depression, Smartphone Dependency according to Subject Characteristics

      Table 3 The Correlations among Depression, Smartphone Dependency, Career Decision-making Self-efficacy in the Subjects

      Table 4 Influencing Factors on Career Decision-making Self-efficacy

      Table 1

      Table 2

      Table 3

      Table 4

      RCPHN : Research in Community and Public Health Nursing
      Editorial Office
      College of Nursing, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
      E-mail: office@rcphn.org

      © Korean Academy of Community Health Nursing.
      TOP