
Significant Factors in Using Contraceptives among Married Women in Cagayan de Oro City using Binary Logistic Regression
Author(s) -
Jessyl D. Orlanes,
Kennet G. Cuarteros
Publication year - 2019
Publication title -
canadian journal of family and youth
Language(s) - English
Resource type - Journals
ISSN - 1718-9748
DOI - 10.29173/cjfy29498
Subject(s) - family planning , logistic regression , abortion , medicine , demography , population , poverty , marital status , fertility , pregnancy , environmental health , research methodology , sociology , economic growth , biology , economics , genetics
Family planning is a larger concept involving preparation and knowledge around a “family future”. It allows people to attain their desired number of children and determine the spacing of pregnancies, reduces the need for abortion, especially unsafe abortion. On the other hand, contraceptives are the group of methods you use or steps you take to avoid pregnancy before you are ready. Contraceptives, one of the methods of family planning, helps prevent the transmission of other sexually transmitted infections. Moreover, it can help slow down population growth thereby contributing to economic benefits such as poverty reduction. It is also a very helpful way to improve the health of mothers and childrens through birth spacing and avoiding high risk pregnancies. In this study, significant factors in using contraceptives are determined. Based on the results from the conducted survey, three out of ten variables were considered as significant factors namely: desire of having more children, religion, and employment status (having p-values of 0.005, 0.008, and 0.000 respectively). These significant factors were used in formulating the model to predict the probability of using contraceptives among married women. Using Hosmer and Lemeshow Test of goodness-of-fit, the p-value of the model is 0.728. Thus, the model is a good fit. A re-survey was conducted to validate the model and 88% of the married women were correctly classified. Hence, the model will be very useful in predicting the probability of contraceptive use among married women.