Korean J Women Health Nurs.  2023 Sep;29(3):219-228. 10.4069/kjwhn.2023.08.17.

Does a preterm labor-assessment algorithm improve preterm labor-related knowledge, clinical practice confidence, and educational satisfaction?: a quasi-experimental study

• Choi HY
• Kim JI

• KMID: 2547157
• DOI: http://doi.org/10.4069/kjwhn.2023.08.17

Abstract

Purpose
Preterm birth is increasing, and obstetric nurses should have the competency to provide timely care. Therefore, training is necessary in the maternal nursing practicum. This study aimed to investigate the effects of practice education using a preterm-labor assessment algorithm on preterm labor-related knowledge and clinical practice confidence in senior nursing students. Methods: A pre-post quasi-experimental design with three groups was used for 61 students. The preterm-labor assessment algorithm was modified into three modules from the preterm-labor assessment algorithm by March of Dimes. We evaluated preterm labor-related knowledge, clinical practice confidence, and educational satisfaction. Data were analyzed with the paired t-test and repeated-measures analysis of variance. Results: The practice education using a preterm-labor assessment algorithm significantly improved both preterm labor-related knowledge and clinical practice confidence (paired t=–7.17, p<.001; paired t=–5.51, p<.001, respectively). The effects of the practice education using a preterm-labor assessment algorithm on knowledge lasted until 8 weeks but decreased significantly at 11 and 13 weeks after the program, while the clinical practice confidence significantly decreased at 8 weeks post-program. Conclusion: The practice education using a preterm-labor assessment algorithm was effective in improving preterm labor-related knowledge and clinical practice confidence. The findings suggest that follow-up education should be conducted at 8 weeks, or as soon as possible thereafter, to maintain knowledge and clinical confidence, and the effects should be evaluated.

Keyword

Algorithms; Clinical competence; Knowledge; Premature obstetric labor; 알고리즘; 임상수행; 지식; 조기진통

Figure

• 

  Figure 1. Changes in three groups over time (t-value: T1–T2 difference). (A) Preterm labor-related knowledge. (B) Clinical practice confidence.

Reference

References

1. Shin KA, Cho BH. Professional self-concept, critical thinking disposition and clinical competence in nursing students. J Korean Acad Fundam Nurs. 2012; 19(1):46–56. https://doi.org/10.7739/jkafn.2012.19.1.046.

2. Hur HK, Park SM, Shin YH, Lim YM, Kim GY, Kim KK, et al. Development and applicability evaluation of an emergent care management simulation practicum for nursing students. J Korean Acad Soc Nurs Educ. 2013; 19(1):228–240. https://doi.org/10.5977/jkasne.2013.19.2.228.

3. Park K, Seo KW, Jeon YH, Song YS. Integrative review for simulation based learning research: 2015~2016 year. J Korea Soc Simul Nurs. 2016; 4(1):41–58.

4. Park KY. Trend analysis of studies on simulation-based education for delivery nursing: based on Jeffries’s simulation model. J Soc Converg Stud. 2020; 4(6):45–54. https://doi.org/10.37181/JSCS.2020.4.6.045.

5. Hwang JY. Reclassification of high-risk pregnancy for maternal-fetal healthcare providers. J Korean Soc Matern Child Health. 2020; 24(2):65–74. https://doi.org/10.21896/jksmch.2020.24.2.65.

6. Kim JI, et al. Women’s Health Education Committee. Women’s health nursing II: Maternal nursing. 10th ed. Paju: Soomoonsa;2020.

7. Iams JD. Prediction and early detection of preterm labor. Obstet Gynecol. 2003; 101(2):402–412. https://doi.org/10.1016/s0029-7844(02)02505-x.

8. Kang HJ, Kwon HS, Sohn IS, Hwang HS. The risk factor related with small for gestational age in term delivery women with an episode of preterm labor. Korean J Obstet Gynecol. 2012; 55(5):293–299. https://doi.org/10.5468/KJOG.2012.55.5.293.

9. Doyle J, Silber A. Preterm labor: role of the nurse practitioner. Nurse Pract. 2015; 40(3):49–54. https://doi.org/10.1097/01.NPR.0000445957.28669.51.

10. Griggs KM, Hrelic DA, Williams N, McEwen-Campbell M, Cypher R. Preterm labor and birth: a clinical review. MCN Am J Matern Child Nurs. 2020; 45(6):328–337. https://doi.org/10.1097/NMC.0000000000000656.

11. Hedriana H, Byrne J, Campbell Bliss M, Kowalewsk L, Georges M, Lombardo V. March of Dimes preterm labor assessment toolkit. White Plains (NY): March of Dimes;2013.

12. Dickerson SS, Sackett K, Jones JM, Brewer C. Guidelines for evaluating tools for clinical decision making. Nurse Educ. 2001; 26(5):215–220. https://doi.org/10.1097/00006223-200109000-00010.

13. Brier J, Carolyn M, Haverly M, et al. Knowing 'something is not right' is beyond intuition: development of a clinical algorithm to enhance surveillance and assist nurses to organise and communicate clinical findings. J Clin Nurs. 2015; 24(5-6):832–843. https://doi.org/10.1111/jocn.12670.

14. Chung CW, Kim HS, Park YS. Effects of high-fidelity simulation-based education on maternity nursing. Perspect Nurs Sci. 2011; 8(2):86–96.

15. Park SA, Kim HY. Development and effects of a labor nursing education program using a high-fidelity simulator for nursing students. Korean J Women Health Nurs. 2020; 26(3):240–249. https://doi.org/10.4069/kjwhn.2020.09.18.

16. Kim JI. Development of algorithm for preterm labor manage­ment. In: Proceeding book of 47th Academic Conference of Korean Society of Maternal and Child Health; 2020 Nov 28; Online. Korean Society of Maternal and Child Health; 2016. p. 120.

17. Kim JI. Basic education program toward nursing competency at maternal-fetal intensive care unit: preterm labor pregnancy assessment and management based on decision making algo­rithm. In: Continuing education materials by the Korean Nursing Association: High-risk pregnancy intensive care unit nursing competency basic education program; 2021 Sep 4; Seoul, Korea. Seoul: Korean Nursing Association, Soonchunhyang University Hospital Seoul, Korean Society of Maternal and Child Health; 2021.

18. Lee SK, Kim SH, Park SN. Persistence of integrated nursing simulation program effectiveness. J Korean Acad Fundam Nurs. 2018; 23(3):283–291. https://doi.org/10.7739/jkafn.2016.23.3.283.

19. Yoon MO. The effect and continuity of AHA basic life support (BLS) education on cardiopulmonary resuscitation knowledge, attitude, self-efficacy, and performance of nursing students. Asia-Pac J Multimed Serv Converg Art Humanit Sociol. 2018; 8(1):737–747. http://doi.org/10.35873/ajmahs.2018.8.1.073.

20. Lynn MR. Determination and quantification of content validity. Nurs Res. 1986; 35(6):382–385.

21. Schwirian PM. Evaluating the performance of nurses: a multidimensional approach. Nurs Res. 1978; 27(6):347–351.

22. Park SJ, Ji ES. A structural model on the nursing competencies of nursing simulation learners. J Korean Acad Nurs. 2018; 48(5):588–600. https://doi.org/10.4040/jkan.2018.48.5.588.

23. Song MK. Hong SU. A survey of students’ satisfaction on participation learning using role-play in clerkship. J Korean Med Ophthalmol Otolaryngol Dermatol. 2012; 25(3):65–77. https://doi.org/10.6114/jkood.2012.25.3.065.

24. Lee HJ, Chun NM, Noh GO, Song HJ, Kim JH, Ha SM. Development of lower extremity lymphedema nursing practice protocol for patients following gynecologic cancer treatment. Asian Oncol Nurs. 2018; 18(3):143–153. https://doi.org/10.5388/aon.2018.18.3.143.

25. Lee JH, Ju HO, Lee YJ. Effects of an algorithm-based education program on nursing care for children with epilepsy by hospital nurses. Child Health Nurs Res. 2019; 25(3):324–332. https://doi.org/10.4094/chnr.2019.25.3.324.

26. Hur HK, Park SM. Effects of simulation based education, for emergency care of patients with dyspnea, on knowledge and performance confidence of nursing students. J Korean Acad Soc Nurs Educ. 2012; 18(1):111–119.

27. Park SJ. Development and Evaluation of a virtual simulation program on nursing care for patients with acute upper gastrointestinal bleeding. [master’s thesis]. Seoul: Kyung Hee University;2018. 146.

28. Lee HJ, Park HA. Development of telephone consultation algorithm for patient discharged with ophthalmic disease. J Korean Acad Nurs Adm. 2011; 17(3):336–348. https://doi.org/10.11111/jkana.2011.17.3.336.

29. Shin SO. Clinical judgment force and nursing performance satisfaction by application of simulation-based myocardial infarction education. J Korean Soc Ind Conver. 2019; 22(6):721–727. https://doi.org/10.21289/KSIC.2019.22.6.721.

30. Kim HS, Choi EY. Continuity of BLS training effects in nursing students. J Korean Acad Soc Nurs Educ. 2012; 18(1):102–110. https://doi.org/10.5977/jkasne.2012.18.1.102.

31. Nelissen E, Ersdal H, Mduma E, Evjen-Olsen B, Broerse J, van Roosmalen J, et al. Helping mothers survive bleeding after birth: retention of knowledge, skills, and confidence nine months after obstetric simulation-based training. BMC Pregnancy Childbirth. 2015; 15:190. https://doi.org/10.1186/s12884-015-0612-2.

32. Vermeulen J, Buyl R, D'haenens F, Swinnen E, Stas L, Gucciardo L, et al. Midwifery students’ satisfaction with perinatal simulation-based training. Women Birth. 2021; 34(6):554–562. https://doi.org/10.1016/j.wombi.2020.12.006.