Peripartum Depresyon Belirleyicileri Ölçeği’nin Psikometrik Özellikleri

Abstract

Amaç: Doğumla ilgili en yaygın komplikasyonlardan biri olan doğum sonrası depresyon her beş kadından birini etkilemekte, buna rağmen vakaların yarısı fark edilmemektedir. Bu nedenle, doğum sonrası depresyon riski taşıyan kadınları erken tanılamak için tarama araçlarına ihtiyaç duyulmaktadır. Bu araştırma, Peripartum Depresyon Belirleyicileri Ölçeği’nin geliştirilmesi, geçerlik ve güvenirliğinin incelenmesi amacıyla yapıldı. Yöntem: Bu çalışma metodolojik bir araştırma olarak tasarlandı. Mart-Haziran 2023 tarihleri arasında, Türkiye'de bir eğitim araştırma hastanesinde doğum öncesi takibi yapılan gebe kadınlar (n = 482) araştırmaya katıldı. Ölçeğe ilişkin güvenirlik analizleri Cronbach Alpha ve yarıya bölme yöntemi ile incelendi. Yapı geçerliliği için Açıklayıcı ve Doğrulayıcı Faktör Analizi uygulandı. Bulgular: Madde toplam puan korelasyon değerleri 0,30’un altında kalan maddeler analizden çıkarıldı. Açıklayıcı faktör analizi sonucunda 21 maddeden oluşan beş faktörlü yapı elde edildi. Ölçeğin toplam açıklanan varyansı %55,785 olarak belirlendi. Birinci düzey çok faktör analizi sonuçlarına göre ölçeğin uyum iyiliği indeksleri mükemmel uyum gösterdi (RMSEA = 0,044; χ2(CMIN/DF) = 1,923; GFI = 0,972; AGFI = 0,964). Ölçeğin Alpha değerinin 0,836 ve alt boyutlara ilişkin Alpha değerlerinin 0,603-0,825 arasında olduğu belirlendi. Sonuç: Peripartum Depresyon Belirleyicileri Ölçeği’nin geçerli ve güvenilir bir ölçüm aracı olduğu belirlendi. Ölçek, gebelik sürecinin herhangi bir döneminde peripartum depresyon belirleyicilerini tanılamak ve postpartum depresyon açısından riski saptamak amacıyla kullanılabilir.

Keywords

• Gebelik
• Geçerlik
• Güvenirlik
• Ölçek geliştirme
• Peripartum depresyon
• Psikometrik

Psychometric Properties of the Peripartum Depression Predictors Scale

Abstract

Objective: Postpartum depression, one of the most common childbirth-related complications, affects one in five women, yet half of the cases go unnoticed. Therefore, screening tools are needed to early identify women at risk of postpartum depression. The present research was conducted to develop the Peripartum Depression Predictors Scale (PDPS) and assess its validity and reliability. Methods: This study was designed as methodological research. Pregnant women (n = 482) who underwent prenatal care at a training and research hospital in Türkiye between March and June 2023 participated in the study. The reliability analyses of the scale were performed using Cronbach's alpha and the split-half reliability coefficients. Exploratory and confirmatory factor analyses were conducted for construct validity. Results: The items with total correlation values lower than 0.30 were excluded from the analysis. A five-factor structure consisting of 21 items was obtained through exploratory factor analysis. The explained variance of the scale was determined as 55.785%. According to the results of the first-order multifactor analysis, the goodness of fit indices of the scale indicated a perfect fit (RMSEA = 0.044; χ2(CMIN/DF) = 1.923; GFI = 0.972; AGFI = 0.964). The Cronbach’s alpha value was 0.836 for the entire scale and ranged between 0.603 and 0.825 for the subscales. Conclusion: It was found that the PDPS is a valid and reliable measurement instrument. The scale can be utilized to determine the predictors of peripartum depression during any stage of pregnancy and to identify the risk for postpartum depression.

Keywords

• Peripartum depression
• pregnancy
• psychometrics
• reliability
• scale development
• validity

References

1. Alves, S., Fonseca, A., Canavarro, M. C., & Pereira, M. (2019). Predictive validity of the Postpartum Depression Predictors Inventory-Revised (PDPI-R): A longitudinal study with Portuguese women. Midwifery, 69, 113-120. https://doi.org/10.1016/j.midw.2018.11.006
2. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Arlington (VA): American Psychiatric Publishing.
3. Ay, F., Tektaş, E., Mak, A., & Aktay, N. (2018). Postpartum depression and the factors affecting it: 2000-2017 study results. Journal of Psychiatric Nursing, 9(3), 147-152. https://doi.org/10.14744/phd.2018.31549
4. Beck, C. T. (2002). Revision of the Postpartum Depression Predictors Inventory. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 31(4), 394-402.
5. Beck, C. T., Records, K., & Rice, M. (2006). Further development of the Postpartum Depression Predictors Inventory‐Revised. Journal of Obstetric, Gynecologic & Neonatal Nursing, 35(6), 735-745. https://doi.org/10.1111/J.1552-6909.2006.00094.x
6. Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6, 149. https://doi.org/10.3389/fpubh.2018.00149
7. Branquinho, M., Shakeel, N., Horsch, A., & Fonseca, A. (2022). Frontline health professionals’ perinatal depression literacy: A systematic review. Midwifery, 111, 103365. https://doi.org/10.1016/j.midw.2022.103365
8. Brownlee, M. H. (2022). Screening for postpartum depression in a neonatal intensive care unit. Advances in Neonatal Care 22(3), E102-E110. https://doi.org/10.1097/ANC.0000000000000971

9. Carpenter, S. (2018). Ten steps in scale development and reporting: A guide for researchers. Communication Methods and Measures, 12(1), 25-44. https://doi.org/10.1080/19312458.2017.1396583
10. Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. The British Journal of Psychiatry,150, 782-786. https://doi.org/10.1192/bjp.150.6.782
11. Davis, L. L. (1992). Instrument review: Getting the most from a panel of experts. Applied Nursing Research, 5(4), 94-197. https://doi.org/10.1016/S0897-1897(05)80008-4
12. Dekel, S., Ein-Dor, T., Ruohomäki, A., Lampi, J., Voutilainen, S., Tuomainen, T. P., Heinonen, S., Kumpulainen, K., Pekkanen, J., Keski-Nisula, L., Pasanen, M., & Lehto, S. M. (2019). The dynamic course of peripartum depression across pregnancy and childbirth. Journal of Psychiatric Research, 113, 72-78. https://doi.org/10.1016/j.jpsychires.2019.03.016
13. DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage. Engindeniz, A.N., Küey, L., & Kültür, S. (1996). Turkish version of the Edinburg Postpartum Depression Scale. Reliability and validity study. Spring Symposiums I book. Ankara: Psychiatry Association Press, p. 51-52.
14. Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967-988. https://doi.org/10.1177/014920639502100509
15. Hooper, D., Coughlan, J., & Mullen, M. R. (20089. Structural equation modelling: Guidelines for determining model fit. Electron J Bus Res Methods, 6, 53-60.
16. Horowitz, J. A., & Goodman, J. H. (2005). Identifying and treating postpartum depression. Journal of Obstetric, Gynecologic & Neonatal Nursing, 34(2), 264-273. https://doi.org/10.1177/0884217505274583
17. Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a reliability coefficient be too high? Journal of Consumer Psychology, 10(1), 55-58.
18. Ibarra-Yruegas, B., Lara, M. A., Navarrete, L., Nieto, L., & Kawas Valle, O. (2018). Psychometric properties of the Postpartum Depression Predictors Inventory-Revised for pregnant women in Mexico. Journal of Health Psychology, 23(11), 1415-1423. https://doi.org/10.1177/1359105316658969
19. Ikeda, M., & Kamibeppu, K. (2013). Measuring the risk factors for postpartum depression: development of the Japanese version of the Postpartum Depression Predictors Inventory-Revised (PDPI-R-J). BMC Pregnancy and Childbirth, 13(1), 1-11. http://www.biomedcentral.com/1471-2393/13/112
20. Li, Z., & Liu, L. (2018). Methods of Nursing Research (2nd ed.). People’s Medical Publishing House, Beijing, China.
21. 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
22. Morin, A. J. S., Arens, A.K., & Marsh, H. W. (2016). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct relevant psychometric multidimensionality. Struct Equ Model Multidiscip Journal, 23, 116-139. https://doi.org/10.1080/10705511.2014.961800
23. Norris, M., & Lecavalier, L. (2010). Evaluating the use of exploratory factor analysis in developmental disability psychological research. Journal of Autism Development and Disorders, 40, 8-20. https://doi.org/10.1007/s10803-009-0816-2
24. O’Hara, M. W., & Segre, L. S. (2014). Perinatal depression across the world: Prevalence, risk factors, and detection in primary care. Perinatal Psychiatry. Oxford University Press.
25. Oppo, A., Mauri, M., Ramacciotti, D., Camilleri, V., Banti, S., Borri, C., Rambelli, C., Montagnani, M. S., Cortopassi, S., Bettini, A., Ricciardulli, S., Montaresi, S., Rucci, P., Beck, C. T., & Cassano, G. B. (2009). Risk factors for postpartum depression: the role of the Postpartum Depression Predictors Inventory-Revised (PDPI-R). Archives of Women's Mental Health, 12(4), 239-249. https://doi.org/10.1007/s00737-009-0071-8
26. Price, L. R. (2017). Psychometric Methods: Theory into Practice. New York: The Guilford Press.
27. Records, K., Rice, M., & Beck, C. T. (2007). Psychometric assessment of the postpartum depression predictors inventory-revised. Journal of Nursing Measurement, 15(3),189-202. https://doi.org/10.1891/106137407783095775
28. Saharoy, R., Potdukhe, A., Wanjari, M., & Taksande, A. B. (2023). Postpartum depression and maternal care: exploring the complex effects on mothers and infants. Cureus, 15(7), e41381. https://doi.org/10.7759/cureus.41381
29. Schinka, J. A., Velicer, W. F., & Weiner, I.R. (2012). Handbook of Psychology, Vol. 2, Research Methods in Psychology. Hoboken, NJ: John Wiley & Sons, Inc.
30. Segre, L. S., O’Hara, M. W., Arndt, S., & Beck, C. T. (2010). Screening and counseling for postpartum depression by nurses: the women’s views. MCN The American Journal of Maternal Child Nursing, 35(5), 280-285. https://doi.org/10.1097/NMC.0b013e3181e62679
31. Simon, D., Kriston, L., Loh, A., Spies, C., Scheibler, F., Wills, C., & Härter, M. (2010). Confirmatory factor analysis and recommendations for improvement of the Autonomy‐Preference‐Index (API). Health Expectations, 13(3), 234-243. https://doi.org/10.1111/j.1369-7625.2009.00584.x
32. Slomian, J., Honvo, G., Emonts, P., Reginster, J. Y., & Bruyère, O. (2019). Consequences of maternal postpartum depression: A systematic review of maternal and infant outcomes. Women's Health, 15, 1-55. https://doi.org/10.1177/1745506519844044
33. Stewart, D. E., Robertson, E., Dennis, C. L., Grace, S. L., & Wallington, T. (2003). Postpartum depression: Literature review of risk factors and interventions. Toronto: University Health Network Women’s Health Program for Toronto Public Health.
34. Tabachnick, B., & Fidell, L. S. (2015). Using Multivariate Statistics (6th ed.). New York: Pearson Longman.
35. Valadares, G., Drummond, A. V., Rangel, C. C., Santos, E., & Apter, G. (2020). Maternal mental health and peripartum depression. Women's Mental Health: A Clinical and Evidence-Based Guide, 349-375. https://doi.org/10.1007/978-3-030-29081-8_24
36. Wang, Z., Liu, J., Shuai, H., Cai, Z., Fu, X., Liu, Y., Xiao, X., Zhang, W., Krabbendam, E., Liu, S., Liu, Z., Li, Z., & Yang, B. X. (2021). Mapping global prevalence of depression among postpartum women. Translational Psychiatry, 11(1), 543. https://doi.org/10.1038/s41398-021-01663-6
37. Youn, J. H., & Jeong, I. S. (2011). Predictive validity of the Postpartum Depression Predictors Inventory-Revised. Asian Nursing Research, 5(4), 210-215. https://doi.org/10.1016/j.anr.2011.11.003