To analyze the problem of breastfeeding, one factor impacting exclusive breast milk feeding will be considered. Postnatal breastfeeding support impact will be evaluated in the postpartum period to determine how it affects breast milk feeding rates. In particular, interpersonal communication between nurses and patients will be provided as a two-way and active dialogue. In the postpartum period, women will be asked whether they received the education within the last 30 weeks of gestation.
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Determination of Data
The following metric will be measured: PC-05 Exclusive Breast Milk Feeding. Data period will include a sample of 70 newborns discharged in October 2019. The compliance rate is to be 16.7 % – 9 of 54 patients; 16 excluded (see category B patients), with 17.9 % Compliance in October 2019 and 12.4 % in December 2019. The project length will be three months, which is considered to be sufficient for revealing the changes that are likely to appear after the implementation of the identified intervention.
The descriptive statistics will be applied to examine breastfeeding compliance, demographic data, and medical indicators (Harvey & Land, 2016). SPSS statistics instrument will be used due to the need to calculate frequencies and percentages. In particular, means, proportions, counts, and standard deviations are to be determined. The first analysis will focus on the variable of exclusive breastfeeding (ordinal) that will be calculated by means of proportion to understand different outcomes of breastfeeding. The second analysis will be based on a chi-square test for independent variables, including mothers’ demographic data (nominal) (Sharpe, 2015).
The exclusivity outcome of the first analysis will provide an understanding of continuous breast feeding and the associated factor of education and support. Furthermore, the identified variable will be analyzed by conducting a one-way ANOVA test to identify statistically significant differences (LoBiondo-Wood & Haber, 2018). The demographic variable will include the age differences of mothers. The mentioned data is to be analyzed, focusing on the mean characteristics.
In order to assess the effectiveness of the interventions, an effect size will be used to understand their magnitude. The value of statistics that is to be calculated based on the sample data would provide awareness of this parameter. In particular, the mean difference and correlation between two variables can be used to calculate the effect size for the proposed project (Pituch & Stevens, 2016).
Rational for Selecting the Methods and Clinical Significance
The selected evaluation method is expected to either verify or reject the perceived importance of promoting exclusive breastfeeding in the postpartum period. In particular, the benchmark data analysis will be useful to determine the extent to which support from nurses impacts the rate of exclusive breastfeeding. It is expected that greater attention to the needs of women will stimulate their compliance with the official breastfeeding recommendations.
The chosen methods of the statistical analysis present the opportunity to improve patient care. The patterns that affect exclusive breastfeeding will be determined, and the behaviors of patients will be understood better. The use of statistics will show the effectiveness of postpartum support and clarify whether this intervention should be used in the future practice or not. At a larger scale, the application of the statistical analysis will allow enhancing the nursing profession, making it relevant to the changing needs of patients.
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In terms of clinical significance, it is considered that infants until six months of age are to be given only breast milk that contains all the necessary vitamins and minerals (Rosuzeita, Rabiaah, Rohani, & Shukri, 2018). Since many women still lack a proper understanding of the role of breastfeeding, support from the staff can be a feasible and viable way to improve this situation. It should also be stressed that the implementation of breastfeeding programs is promoted by the World Health Organization (WHO) and other global organizations (Rosuzeita et al., 2018).
Projected Number of Participants
For the proposed project, the application of purposive sampling allowed selecting potential mothers. It is decided to focus on primigravidas since they compose the most unaware category of patients. Considering the purpose of the project, its timeline, and available instruments, mothers of 70 newborns discharged in October, 2019 will be offered to contribute to this study.
It is planned to divide them into control and intervention groups to compare the outcomes of prenatal education and postpartum support. After signing their informed consent forms, both groups should include a similar number of participants. The eligibility criteria are the first pregnancy, having the intention to breastfeed, and a lack of illnesses that contraindicate breastfeeding.
To achieve the identified number of participants, current patients of hospitals will be given booklets with brief information about the project. Their potential contribution to the proposed study will be clarified to avoid any misconceptions. All sensitive data will be kept confidential, and ethical considerations will be taken into account. The mentioned sample size will allow evaluating the link between demographic data and education / support impact on exclusive breastfeeding.
Harvey, M., & Land, L. (2016). Research methods for nurses and midwives: Theory and practice. New York, NY: Sage.
LoBiondo-Wood, G., & Haber, J. (2018). Nursing research: Methods and critical appraisal for evidence-based practice (9th ed.). New York, NY: Elsevier Health Sciences.
Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS (6th ed.). New York, NY: Routledge.
Rosuzeita, F., Rabiaah, M. C., Rohani, I., & Shukri, O. M. (2018). The effectiveness of breastfeeding intervention on breastfeeding exclusivity and duration among primiparous mothers in Hospital University Sains Malaysia. The Malaysian Journal of Medical Sciences, 25(1), 53-66.
Sharpe, D. (2015). Chi-square test is statistically significant: Now what?. Practical Assessment, Research, and Evaluation, 20(8), 1-10.