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Maternal depression (MD) was one of the most prevalent psychiatric problems worldwide. However, it easily remains untreated and misses the best time to prevent the emergence or worsening of major depressive symptoms due to under-observed stigma and the lack of effective screening tools. Thus, this study aims to develop and validate a machine learning-based MD symptoms prediction model integrating more observable and objective factors to early detect and monitor MD risk. A cross-sectional study was conducted in 10 community vaccination centers in Wenzhou, China, and a total of 1099 mothers were surveyed by using purposive sampling. A questionnaire containing questions regarding socio-demographic variables, psychophysiological variables, wife role-related variables, and mother role-related variables was used to collect data. A framework of data preprocessing, feature selection, and model evaluation was implemented to develop an optimal risk prediction model. Results demonstrated that the XG-Boost algorithm provided robust performance with the highest AUC and well-balanced sensitivity and specificity (AUC = 0.90, sensitivity = 0.74, specificity = 0.90). Furthermore, the causal mediation analysis indicated that wife-mother role conflict positively predicted MD symptoms, and it also exerted influence on mothers suffering through the mediation of anxiety and insomnia. Findings from the present study may help guide the development of MD screening tools to early detect and provide the modifiable risk factor information for timely tailored prevention.

Titel:	Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China.
Autor/in / Beteiligte Person:	Hong, L ; Yang, A ; Liang, Q ; He, Y ; Wang, Y ; Tao, S ; Chen, L
Link:	Full Text Finder (Volltext)
Zeitschrift:	Prevention science : the official journal of the Society for Prevention Research, Jg. 25 (2024-05-01), Heft 4, S. 699-710
Veröffentlichung:	New York, NY : Kluwer Academic/Plenum Publishers, c2000-, 2024
Medientyp:	academicJournal
ISSN:	1573-6695 (electronic)
DOI:	10.1007/s11121-023-01610-5
Schlagwort:	Humans Female China Adult Cross-Sectional Studies Depression Spouses psychology Surveys and Questionnaires Role Conflict Machine Learning Mothers psychology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Prev Sci] 2024 May; Vol. 25 (4), pp. 699-710. <i>Date of Electronic Publication: </i>2023 Oct 28. MeSH Terms: Machine Learning* ; Mothers* / psychology ; Humans ; Female ; China ; Adult ; Cross-Sectional Studies ; Depression ; Spouses / psychology ; Surveys and Questionnaires ; Role Conflict References: Abdollahi, F., Agajani-Delavar, M., Zarghami, M., & Lye, M.-S. (2016). Postpartum mental health in first-time mothers: A cohort study. Iranian Journal of Psychiatry and Behavioral Sciences, 10(1). https://doi.org/10.17795/ijpbs-426. ; Amit, G., Girshovitz, I., Marcus, K., Zhang, Y., Pathak, J., Bar, V., & Akiva, P. (2021). Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy and Childbirth, 21(1), 630. https://doi.org/10.1186/s12884-021-04087-8. (PMID: 10.1186/s12884-021-04087-8345351168447665) ; Andersson, S., Bathula, D. R., Iliadis, S. I., Walter, M., & Skalkidou, A. (2021). Predicting women with depressive symptoms postpartum with machine learning methods. Scientific Reports, 11(1), 1–15. https://doi.org/10.1038/s41598-021-86368-y. (PMID: 10.1038/s41598-021-86368-y) ; Bastien, C. H., Vallières, A., & Morin, C. M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine, 2(4), 297–307. https://doi.org/10.1016/s1389-9457(00)00065-4. (PMID: 10.1016/s1389-9457(00)00065-411438246) ; Beck, C. T. (2006). Postpartum depression: It isn’t just the blues. AJN the American Journal of Nursing, 106(5), 40–50. (PMID: 10.1097/00000446-200605000-0002016905931) ; Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern Recognition and Machine Learning (Vol. 4). Springer. ; Black, M. M., Walker, S. P., Fernald, L. C., Andersen, C. T., DiGirolamo, A. M., Lu, C., McCoy, D. C., Fink, G., Shawar, Y. R., & Shiffman, J. (2017). Early childhood development coming of age: Science through the life course. The Lancet, 389(10064), 77–90. https://doi.org/10.1016/S0140-6736(16)31389-7. (PMID: 10.1016/S0140-6736(16)31389-7) ; Bodnar-Deren, S., Benn, E. K. T., Balbierz, A., & Howell, E. A. (2017). Stigma and postpartum depression treatment acceptability among Black and White women in the first six-months postpartum. Maternal and Child Health Journal, 21(7), 1457–1468. https://doi.org/10.1007/s10995-017-2263-6. (PMID: 10.1007/s10995-017-2263-628102504) ; Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. (PMID: 10.1023/A:1010933404324) ; Burke, W., & Abidin, R. (1980). Parenting stress index (PSI): A family system assessment approach. Parent Education and Intervention Handbook, 516–527. ; Chen, B. B., Qu, Y., Yang, B., & Chen, X. (2022). Chinese mothers’ parental burnout and adolescents’ internalizing and externalizing problems: The mediating role of maternal hostility. Developmental Psychology, 58(4), 768–777. https://doi.org/10.1037/dev0001311. (PMID: 10.1037/dev000131134941305) ; Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining,. ; Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150, 782–786. https://doi.org/10.1192/bjp.150.6.782. (PMID: 10.1192/bjp.150.6.782) ; Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 1–25. https://doi.org/10.1186/s40537-019-0217-0. (PMID: 10.1186/s40537-019-0217-0) ; Eifert, G. H., Forsyth, J. P., Arch, J., Espejo, E., Keller, M., & Langer, D. (2009). Acceptance and commitment therapy for anxiety disorders: Three case studies exemplifying a unified treatment protocol. Cognitive and Behavioral Practice, 16(4), 368–385. https://doi.org/10.1016/j.cbpra.2009.06.001. (PMID: 10.1016/j.cbpra.2009.06.001) ; Fang, H., Tu, S., Sheng, J., & Shao, A. (2019). Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment. Journal of Cellular and Molecular Medicine, 23(4), 2324–2332. https://doi.org/10.1111/jcmm.14170. (PMID: 10.1111/jcmm.14170307344866433686) ; Gdańska, P., Drozdowicz-Jastrzębska, E., Grzechocińska, B., Radziwon-Zaleska, M., Węgrzyn, P., & Wielgoś, M. (2017). Anxiety and depression in women undergoing infertility treatment. Ginekologia Polska, 88(2), 109–112. https://doi.org/10.5603/GP.a2017.0019. (PMID: 10.5603/GP.a2017.001928326521) ; Goco, D. E. B., & Diliman, Q. C. (2019). Experiences of mothers from dual-career families on child-rearing of their preschool-aged children. Signature. ; Gonzalez, O., & Valente, M. J. (2022). Accommodating a latent XM interaction in statistical mediation analysis. Multivariate Behavioral Research, 1–16. https://doi.org/10.1080/00273171.2022.2119928. ; Goodman, J. H. (2004). Postpartum depression beyond the early postpartum period. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 33(4), 410–420. https://doi.org/10.1177/0884217504266915. (PMID: 10.1177/0884217504266915) ; Hobfoll, S. E. (1989). Conservation of resources. A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. https://doi.org/10.1037//0003-066x.44.3.513. ; Jacques, N., de Mola, C. L., Joseph, G., Mesenburg, M. A., & da Silveira, M. F. (2019). Prenatal and postnatal maternal depression and infant hospitalization and mortality in the first year of life: A systematic review and meta-analysis. Journal of Affective Disorders, 243, 201–208. https://doi.org/10.1016/j.jad.2018.09.055. (PMID: 10.1016/j.jad.2018.09.05530245252) ; Jansson-Fröjmark, M., & Lindblom, K. (2008). A bidirectional relationship between anxiety and depression, and insomnia? A prospective study in the general population. Journal of Psychosomatic Research, 64(4), 443–449. https://doi.org/10.1016/j.jpsychores.2007.10.016. (PMID: 10.1016/j.jpsychores.2007.10.01618374745) ; Jerath, R., Beveridge, C., & Barnes, V. A. (2019). Self-regulation of breathing as an adjunctive treatment of insomnia [perspective]. Frontiers in Psychiatry, 9. https://doi.org/10.3389/fpsyt.2018.00780. ; Johnson, D. R., White, L. K., Edwards, J. N., & Booth, A. (1986). Dimensions of marital quality: Toward methodological and conceptual refinement. Journal of Family Issues, 7(1), 31–49. https://doi.org/10.1177/019251386007001003. (PMID: 10.1177/019251386007001003) ; Johnston, C., & Mash, E. J. (1989). A measure of parenting satisfaction and efficacy. Journal of Clinical Child Psychology, 18(2), 167–175. https://doi.org/10.1207/s15374424jccp1802_8. (PMID: 10.1207/s15374424jccp1802_8) ; Judd, E. R. (1994). Gender and power in rural North China. Stanford University Press. ; Kang, L., Jing, W., Liu, J., Ma, Q., Zhang, S., & Liu, M. (2022). The prevalence of barriers to rearing children aged 0–3 years following China’s new three-child policy: A national cross-sectional study. BMC Public Health, 22(1), 1–10. https://doi.org/10.1186/s12889-022-12880-z. (PMID: 10.1186/s12889-022-12880-z) ; Karatzoglou, A., Smola, A., Hornik, K., & Karatzoglou, M. A. (2016). Package ‘kernlab’. Google Scholar. ; Kotsiantis, S., & Pintelas, P. (2004). Combining bagging and boosting. International Journal of Computational Intelligence, 1(4), 324–333. ; Kraus, C., Kadriu, B., Lanzenberger, R., Zarate, C. A., Jr., & Kasper, S. (2019). Prognosis and improved outcomes in major depression: A review. Translational Psychiatry, 9(1), 127. https://doi.org/10.1038/s41398-019-0460-3. (PMID: 10.1038/s41398-019-0460-3309443096447556) ; Lapato, D. M., Roberson-Nay, R., Kinser, P. A., & York, T. P. (2020). Predictive validity of a DNA methylation-based screening panel for postpartum depression. medRxiv. https://doi.org/10.1101/2020.03.05.20027847v1. ; Lavner, J. A., & Bradbury, T. N. (2010). Patterns of change in marital satisfaction over the newlywed years. Journal of Marriage and Family, 72(5), 1171–1187. https://doi.org/10.1111/j.1741-3737.2010.00757.x. ; Lazarus, R. S. (2000). Evolution of a model of stress, coping, and discrete emotions. Handbook of stress, coping, and health: Implications for nursing research, theory, and practice, 195–222. ; Lee, D. T., & Chung, T. K. (2007). Postnatal depression: An update. Best Practice & Research Clinical Obstetrics & Gynaecology, 21(2), 183–191. https://doi.org/10.1016/j.bpobgyn.2006.10.003. (PMID: 10.1016/j.bpobgyn.2006.10.003) ; Letourneau, N. L., Dennis, C.-L., Benzies, K., Duffett-Leger, L., Stewart, M., Tryphonopoulos, P. D., Este, D., & Watson, W. (2012). Postpartum depression is a family affair: Addressing the impact on mothers, fathers, and children. Issues in Mental Health Nursing, 33(7), 445–457. https://doi.org/10.3109/01612840.2012.673054. (PMID: 10.3109/01612840.2012.67305422757597) ; Liu, X., Wang, S., & Wang, G. (2022). Prevalence and risk factors of postpartum depression in women: A systematic review and meta-analysis. Journal of Clinical Nursing, 31(19–20), 2665–2677. https://doi.org/10.1111/jocn.16121. (PMID: 10.1111/jocn.1612134750904) ; MacKinnon, D. P., Valente, M. J., & Gonzalez, O. (2020). The correspondence between causal and traditional mediation analysis: The link is the mediator by treatment interaction. Prevention Science, 21(2), 147–157. https://doi.org/10.1007/s11121-019-01076-4. (PMID: 10.1007/s11121-019-01076-4318330216992469) ; Mickelson, K. D., Biehle, S. N., Chong, A., & Gordon, A. (2017). Perceived stigma of postpartum depression symptoms in low-risk first-time parents: Gender differences in a dual-pathway model. Sex Roles, 76(5), 306–318. https://doi.org/10.1007/s11199-016-0603-4. (PMID: 10.1007/s11199-016-0603-4) ; Misra, P., & Yadav, A. S. (2020). Improving the classification accuracy using recursive feature elimination with cross-validation. International Journal of Emerging Technologies in Learning, 11, 659–665. ; Murray, L., Dunne, M. P., Van Vo, T., Anh, P. N., Khawaja, N. G., & Cao, T. N. (2015). Postnatal depressive symptoms amongst women in Central Vietnam: A cross-sectional study investigating prevalence and associations with social, cultural and infant factors. BMC Pregnancy Childbirth, 15, 234. https://doi.org/10.1186/s12884-015-0662-5. ; Neckelmann, D., Mykletun, A., & Dahl, A. A. (2007). Chronic insomnia as a risk factor for developing anxiety and depression. Sleep, 30(7), 873–880. https://doi.org/10.1093/sleep/30.7.873. (PMID: 10.1093/sleep/30.7.873176826581978360) ; Ngai, F. W., & Chan, S. W. (2012). Learned resourcefulness, social support, and perinatal depression in Chinese mothers. Nursing Research, 61(2), 78–85. https://doi.org/10.1097/NNR.0b013e318240dd3f. (PMID: 10.1097/NNR.0b013e318240dd3f22307141) ; NHFPC. (2017). China health statistics yearbook. Retrieved 27 Dec from http://www.stats.gov.cn/tjsj/ndsj/2015/indexch.htm. ; Nomaguchi, K., & House, A. N. (2013). Racial-ethnic disparities in maternal parenting stress: The role of structural disadvantages and parenting values. Journal of Health and Social Behavior, 54(3), 386–404. https://doi.org/10.1177/0022146513498511. (PMID: 10.1177/002214651349851124026535) ; Norhayati, M. N., Hazlina, N. H., Asrenee, A. R., & Emilin, W. M. (2015). Magnitude and risk factors for postpartum symptoms: A literature review. Journal of Affective Disorders, 175, 34–52. https://doi.org/10.1016/j.jad.2014.12.041. (PMID: 10.1016/j.jad.2014.12.04125590764) ; Peng, Y. (2018). Migrant mothering in transition: A qualitative study of the maternal narratives and practices of two generations of rural-urban migrant mothers in Southern China. Sex Roles, 79(1), 16–35. https://doi.org/10.1007/s11199-017-0855-7. (PMID: 10.1007/s11199-017-0855-7) ; Rotheram-Fuller, E. J., Tomlinson, M., Scheffler, A., Weichle, T. W., Hayati Rezvan, P., Comulada, W. S., & Rotheram-Borus, M. J. (2018). Maternal patterns of antenatal and postnatal depressed mood and the impact on child health at 3-years postpartum. Journal of Consulting and Clinical Psychology, 86(3), 218–230. https://doi.org/10.1037/ccp0000281. (PMID: 10.1037/ccp0000281295047915842813) ; Saqib, K., Khan, A. F., & Butt, Z. A. (2021). Machine learning methods for predicting postpartum depression: Scoping review. JMIR Mental Health, 8(11), e29838. https://doi.org/10.2196/29838. (PMID: 10.2196/29838348223378663566) ; Schmidt, S., Roesler, U., Kusserow, T., & Rau, R. (2014). Uncertainty in the workplace: Examining role ambiguity and role conflict, and their link to depression—A meta-analysis. European Journal of Work and Organizational Psychology, 23(1), 91–106. https://doi.org/10.1080/1359432X.2012.711523. (PMID: 10.1080/1359432X.2012.711523) ; Scott, J. T., Prendergast, S., Demeusy, E., McGuire, K., & Crowley, M. (2022). Trends and opportunities for bridging prevention science and US Federal Policy. Prevention Science, 23(8), 1333–1342. https://doi.org/10.1007/s11121-022-01403-2. (PMID: 10.1007/s11121-022-01403-235930099) ; Shatte, A. B., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151. (PMID: 10.1017/S003329171900015130744717) ; Shin, D., Lee, K. J., Adeluwa, T., & Hur, J. (2020). Machine learning-based predictive modeling of postpartum depression. Journal of Clinical Medicine, 9(9). https://doi.org/10.3390/jcm9092899. ; Soe, N. N., Wen, D. J., Poh, J. S., Li, Y., Broekman, B. F., Chen, H., Chong, Y. S., Kwek, K., Saw, S.-M., & Gluckman, P. D. (2016). Pre-and post-natal maternal depressive symptoms in relation with infant frontal function, connectivity, and behaviors. PLoS ONE, 11(4), e0152991. https://doi.org/10.1371/journal.pone.0152991. (PMID: 10.1371/journal.pone.0152991270738814830615) ; Solomon, M. R., Surprenant, C., Czepiel, J. A., & Gutman, E. G. (1985). A role theory perspective on dyadic interactions: The service encounter. Journal of Marketing, 49(1), 99–111. https://doi.org/10.1177/002224298504900110. (PMID: 10.1177/002224298504900110) ; Straus, M. A., Hamby, S. L., Boney-McCoy, S., & Sugarman, D. B. (1996). The revised conflict tactics scales (CTS2) development and preliminary psychometric data. Journal of Family Issues, 17(3), 283–316. https://doi.org/10.1177/019251396017003001. (PMID: 10.1177/019251396017003001) ; Tsao, Y., Creedy, D. K., & Gamble, J. (2015). Prevalence and psychological correlates of postnatal depression in rural Taiwanese women. Health Care for Women International, 36(4), 457–474. https://doi.org/10.1080/07399332.2014.946510. (PMID: 10.1080/07399332.2014.94651025148390) ; Woolhouse, H., Gartland, D., Mensah, F., & Brown, S. (2015). Maternal depression from early pregnancy to 4 years postpartum in a prospective pregnancy cohort study: Implications for primary health care. BJOG: An International Journal of Obstetrics & Gynaecology, 122(3), 312–321. https://doi.org/10.1111/1471-0528.12837. ; Xiong, J., Fang, Q., Chen, J., Li, Y., Li, H., Li, W., & Zheng, X. (2021). States transitions inference of postpartum depression based on multi-state Markov model. International Journal of Environmental Research and Public Health, 18(14), 7449. https://doi.org/10.3390/ijerph18147449. (PMID: 10.3390/ijerph18147449342998998304364) ; Zhang, H.-P., & Tsang, K.-M. (2010). The influence of urban wives’ relative income and education on marital quality. Chinese Journal of Clinical Psychology, 18(05), 632–634. https://doi.org/10.16128/j.cnki.1005-3611.2010.05.036. ; Zheng, A., & Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. “ O'Reilly Media, Inc.”. ; Zheng, J., Sun, K., Aili, S., Yang, X., & Gao, L. (2022). Predictors of postpartum depression among Chinese mothers and fathers in the early postnatal period: A cross-sectional study. Midwifery, 105, 103233. https://doi.org/10.1016/j.midw.2021.103233. (PMID: 10.1016/j.midw.2021.10323334968820) ; Zhou, Z.-H. (2012). Ensemble methods: Foundations and algorithms. CRC Press. (PMID: 10.1201/b12207) ; Zung, W. W. (1971). A rating instrument for anxiety disorders. Psychosomatics: Journal of Consultation and Liaison Psychiatry. https://doi.org/10.1016/S0033-3182(71)71479-0. Grant Information: 2021R413086 Zhejiang Provincial Science and Technology Innovation Program (New Young Talent Program) for College Students Contributed Indexing: Keywords: Critical child-rearing stage; Machine learning; Maternal depression; Wife-mother role conflict Entry Date(s): Date Created: 20231028 Date Completed: 20240522 Latest Revision: 20240528 Update Code: 20240529

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Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China.