IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2021i1p215-d711193.html
   My bibliography  Save this article

A Quantile Regression Analysis of Factors Associated with First-Time Maternal Fatigue in Korea

Author

Listed:
  • Jeongok Park

    (Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, Seoul 03722, Korea)

  • Chang Gi Park

    (Department of Population Health Nursing Science, College of Nursing, University of Illinois, Chicago, IL 60607, USA)

  • Kyoungjin Lee

    (College of Nursing and Brain Korea 21 FOUR Project, Younsei University, Seoul 03722, Korea)

Abstract

The aim of this cross-sectional study was to identify the factors associated with different percentiles of first-time maternal fatigue. A total of 123 first-time healthy mothers aged 18 years or older participated through an online survey. The fatigue was measured by the Korean version of the fatigue severity scale. Main variables were constructed based on the integrated fatigue model, which included mothers’ sleep quality, parenting stress, the amount of free time mothers have, the number of the child’s night wakings, general characteristics including socioeconomic status, and working status. Quantile regression was used to analyze the associated factors according to the fatigue level of first-time mothers with a young child. The mean age of the mothers and children were 32.11 years and 20.81 months, respectively. Mean fatigue score was 6.16 among the 75% quantile with high fatigue score. Lack of adequate free time in mothers, advanced maternal age, being a housewife, having a moderate income, and frequent night wakings of their child significantly increased fatigue among mothers in the third quantile of fatigue. To reduce fatigue, healthcare providers should focus on exploring ways to reduce maternal sleep disturbance and improve maternal sleep quality.

Suggested Citation

  • Jeongok Park & Chang Gi Park & Kyoungjin Lee, 2021. "A Quantile Regression Analysis of Factors Associated with First-Time Maternal Fatigue in Korea," IJERPH, MDPI, vol. 19(1), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2021:i:1:p:215-:d:711193
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/1/215/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/1/215/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lai, T. L. & Robbins, Herbert & Wei, C. Z., 1979. "Strong consistency of least squares estimates in multiple regression II," Journal of Multivariate Analysis, Elsevier, vol. 9(3), pages 343-361, September.
    2. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Narisetty, Naveen & Koenker, Roger, 2022. "Censored quantile regression survival models with a cure proportion," Journal of Econometrics, Elsevier, vol. 226(1), pages 192-203.
    2. Eliana Christou & Michael G. Akritas, 2019. "Single index quantile regression for censored data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 655-678, December.
    3. repec:iab:iabfme:200709(en is not listed on IDEAS
    4. De Backer, Mickael & El Ghouch, Anouar & Van Keilegom, Ingrid, 2017. "An Adapted Loss Function for Censored Quantile Regression," LIDAM Discussion Papers ISBA 2017003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. R. M. Balan & Ioana Schiopu-Kratina, 2004. "Asymptotic Results with Generalized Estimating Equations for Longitudinal data II," RePAd Working Paper Series lrsp-TRS398, Département des sciences administratives, UQO.
    6. Sungwan Bang & Soo-Heang Eo & Yong Mee Cho & Myoungshic Jhun & HyungJun Cho, 2016. "Non-crossing weighted kernel quantile regression with right censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 100-121, January.
    7. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    8. Norbert Christopeit & Michael Massmann, 2013. "A Note on an Estimation Problem in Models with Adaptive Learning," Tinbergen Institute Discussion Papers 13-151/III, Tinbergen Institute.
    9. Rahul Kapoor & Ron Adner, 2012. "What Firms Make vs. What They Know: How Firms' Production and Knowledge Boundaries Affect Competitive Advantage in the Face of Technological Change," Organization Science, INFORMS, vol. 23(5), pages 1227-1248, October.
    10. Jiang, Rong & Qian, Weimin & Zhou, Zhangong, 2012. "Variable selection and coefficient estimation via composite quantile regression with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 308-317.
    11. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2020. "Linear censored quantile regression: A novel minimum‐distance approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1275-1306, December.
    12. Elke Lüdemann & Ralf Wilke & Xuan Zhang, 2006. "Censored quantile regressions and the length of unemployment periods in West Germany," Empirical Economics, Springer, vol. 31(4), pages 1003-1024, November.
    13. Yuan-chin Chang, 2011. "Sequential estimation in generalized linear models when covariates are subject to errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(1), pages 93-120, January.
    14. Minji Lee & Zhihua Su, 2020. "A Review of Envelope Models," International Statistical Review, International Statistical Institute, vol. 88(3), pages 658-676, December.
    15. Wilke, Ralf A. & Wichert, Laura, 2005. "Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis," ZEW Discussion Papers 05-67 [rev.], ZEW - Leibniz Centre for European Economic Research.
    16. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    17. Bernd Fitzenberger & Ralf Wilke, 2006. "Using quantile regression for duration analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 105-120, March.
    18. Bijwaard, G.E., 2002. "Instrumental variable estimation for duration data," Econometric Institute Research Papers EI 2002-39, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Kris Knox & Eric Blankmeyer & J. Stutzman, 2007. "Technical efficiency in texas nursing facilities: A stochastic production frontier approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 31(1), pages 75-86, March.
    20. Marcelo Cajias & Philipp Freudenreich & Anna Freudenreich, 2020. "Exploring the determinants of real estate liquidity from an alternative perspective: censored quantile regression in real estate research," Journal of Business Economics, Springer, vol. 90(7), pages 1057-1086, August.
    21. Zhou, Xian & You, Jinhong, 2004. "Wavelet estimation in varying-coefficient partially linear regression models," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 91-104, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2021:i:1:p:215-:d:711193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.