Introduction

Recent demographic changes have led to the unprecedented reduction in the rate of fertility in all regions of world. Iran has experienced a steep drop in fertility. There has been a reduction of more than 50% in the rate of fertility not only unique among Muslim countries, such as Iran, but also all over the world (1). The Censuses and statistics of Iran indicated that the total fertility rate reached 1.2 in 2011 from 6.3 children per woman in 1986 (2). According to the World Bank, the population growth of Iran will become less than 1% in 2025, and the Iranian population will decrease to 0.99% in 2025 (3). Recent UN reports are also warning; and according to the low population growth scenario of the UN in 2010, if Iran continues to pursue a demographic substitution and does not plan to balance it, it will experience a population of 31 million in the next 80 years with 47% of the elderly people over the age of 60 (4). A rapid decline in fertility rate, as well as altering the balance of age pyramid, can cause irreparable economic and social damages to Iran (5). Therefore, the decline of tendency towards childbearing is now an important social issue which should be solved in Iran (3).

From a demographic perspective, fertility is the most important determinant of population fluctuations (gender and age structures of population); therefore, the investigation of fertility is more important than other demographic phenomena (death and migration). Fertility can be affected by a variety of cultural, social, and economic factors which certainly affect various societies in different ways (6). Demographic issues are so extensive that cannot be summarized in one specific theory or formula. As a result, population movements have various and contradictory outcomes at different times and places (5). Zhang (2017) emphasized on the tendency to have a son, income status, education level, and number of deliveries as the main factors influencing women’s tendency to fertility in China (7). In a study by Rad et al. (2015) in Tabriz, religious beliefs were reported as the most important factor in the tendency of married women towards fertility (8). Motlagh (2016) also considered age of women, economic problems, and the men’s opposition as the main reasons for the unwillingness to have a child (9).

The women position has also changed due to the changes in the functions of families. Urbanization and higher education of women provide them opportunities to find jobs out of house and help them become independent before and after marriage. Nowadays, women have greater freedom in making decisions about childbearing and child-rearing. Promotion of socio-economic status of women has made them aware of various ways to achieve the birth control by obtaining the necessary knowledge about this issue (10).

According to recent studies, fertility is under the influence of individuals’ choices, as well as social norms, values, and culture (11). Fertility motivation is a complicated issue with cultural, behavioral, and belief roots. It is affected
by changes in the context of demographic transition, as well as economic and social development. Fertility motivations has two dimensions of positive and negative. Positive fertility motivation includes personal reasons of anyone for having children and the fertility enjoyment, birth and childhood, the traditional viewpoint, satisfaction with child rearing, the sense of need and survival, and instrumental values of a child. Negative fertility motivation entails reasons for not having a child, such as fear of being a parent, parental stress, and child care challenges (12, 13).

According to a study conducted by Miller in the US, positive fertility motivation can lead to a higher tendency towards childbearing, the greater number of children, and lower intervals between births (12). Khadivzadeh (2014) also found that there was a positive direct relationship between positive fertility motivation in couples with a tendency to childbearing and ideal number of children (14).

Given the rapid decline in fertility rates of Iran, the prospect of population policy should focus on direct and indirect factors of increasing fertility to overcome the future challenges of the country by determining factors which affect fertility and adopting suitable demographic policies accordingly. Demographic researchers and policymakers face the challenge of fertility development and determinants of fertility behavior for future (6). The present study aimed to investigate childbearing motivations in women referring to the health centers in Sabzevar, Iran, in 2016 and investigate the relationship between fertility motivations and some personal, economic and social factors.

Materials and Methods

This cross-sectional study was conducted on 450 women aged 18-35 referring to health centers in Sabzevar in 2016. The sample size was determined through simple regression model. In this study, the correlation coefficient was equal to 0.1 based on similar studies (13, 14), and the sample size was equal to 450 using PASS 11 software. The 95% confidence level, the expected regression coefficient of 1 in sample, the standard deviation of 1 for the independent variable, and the correlation coefficient of 0.1 were the applied parameters in calculating the sample size. After employing two-step cluster method, two centers were randomly selected from the main 7 health centers in Sabzevar. The participants in the study were selected by convenience sampling from each center based on the number of daily visitors. The inclusion criteria of study were Iranian nationality and residency in Sabzevar, being married, and non-pregnant at the time
of the study. The exclusion criteria were
the history of hysterectomy, tubectomy, or vasectomy in a spouse.

The study was performed after obtaining research approval from the Ethics Committee of Sabzevar University of Medical Sciences (IR.MEDSAB.REC.1395.35), Sabzevar, Iran, making the necessary coordination with authorities of selected health centers. Data was collected from the women referring to the selected health centers of Sabzevar for receiving services by considering the inclusion criteria. In line with ethical consideration, the eligible women were informed about the purpose of the study. Furthermore, written informed consent was obtained from all participants. After describing the method of responding to questionnaires, research units were asked to study the questionnaire and select an answer that was closer to their current state. If participants were illiterate or semi-literate, questionnaires were completed through interviews by the researchers.

Data collection tool of this study included an interview form consisting of two sections, namely demographic characteristics and fertility characteristics, as well as Miller’s fertility motivation questionnaire. Miller’s fertility motivation questionnaire includes two dimen-sions, namely positive and negative motivations. Positive motivation has 30 items with the subscales of fertility enjoyment, birth and childhood, traditional viewpoint, satisfaction with child rearing, sense of need and survival, and instrumental values of a child. On the other hand, negative motivation entails 19 items related to the fear of being a parent, parental stress, and child care challenges. This questionnaire is rated on a four-point Likert scale, ranging from completely disagree to strongly agree, scored from 1 to 4, respectively (12). In a study conducted by Khadivzadeh (2014), the reliability of this questionnaire was confirmed by test-retest method (r=0.98) and Cronbach’s alpha (α=0.94) (14). In the present study, the reliability of this tool was also confirmed by test-retest method (r=0.89). The content and face validity of the translated version of the questionnaire was approved by 10 faculty members of Sabzevar University of Medical Sciences. After considering necessary suggestions and modification, the final version of the questionnaire was utilized in this study.

Data was analyzed through statistical SPSS software (version 22) using descriptive statistics, Kolmogorov-Smirnov, Pearson correlation, Spearman, and the Kruskal-Wallis tests. Since the results of correlation were purely descriptive and cannot be used for interpretation, the regression model was used to determine the related factors to fertility motivations. P

Results

Table 1 shows some characteristics of research units. As can be seen, the mean age of 26.2 years was for women and 30.2 years for men. Moreover, the mean score for the length of
marriage was 5.9 years. A total of 194 subjects (43%) were living in rented houses. The mean values for the number of children, number of pregnancies, and number of deliveries were 1.4, 1.74, and 1.4, respectively. The mean number of abortion was 0.32. Furthermore, 111 subjects (24.6%) had no children and 118 subjects (34.7%) had both male and female children. Moreover, 221 subjects (64.4%) only mentioned the history of vaginal delivery.

Table 2 presents positive and negative fertility motivations of participating women in this study. The mean score of positive fertility motivation and negative motivation in research units were 22.64 and 17.1, respectively. The results of spearman correlation test indicated that there was a significant negative correlation between education level and the overall score of positive fertility motivations (P=0.01). In this regard, the higher the education level of women, the lower the level of their positive fertility motivations. On the other hand, negative motivation has a significant direct relationship with age, number of deliveries, number of pregnancies and number of children (P<0.05). Accordingly, with the increase in the age and number of pregnancies and childbirth, individual motivation is decreased.

There was also a significant negative relationship between the number of abortions and the overall score of negative motivations (P=0.02); therefore, an increase in the number of abortions could decrease negative fertility motivations. Moreover, income level correlated negatively with the overall score of negative fertility motivations (P=0.001); accordingly, an increased level of income led to higher negative fertility motivations of research units (Table 3).

The result of the Kruskal-Wallis test indicated that husbands’ jobs were related to negative fertility motivations. As a result, the highest fertility motivation was seen when the husband was unemployed (P=0.01). There was also a relationship between the child’s gender and negative fertility motivations, meaning that the highest negative fertility motivation was seen in a group with both male and female children (P=0.01). Moreover, types of residence associated with negative fertility motivations, so the least negative fertility motivation was reported in participants residing in their own

personal houses (P=0.01).

According to the study on the correlation between components of fertility motivation and both personal and socioeconomic factors, the results of Pearson and Spearman tests indicated that satisfaction with child rearing in the positive fertility motivation dimension has a significant negative relationships with mothers’ age (r=-0.12, P=0.009), spouse age (r=-0.11, P=0.01), duration of marriage (r=-0.15, P=0.008) and number of children (r=-0.10, P=0.003). Furthermore, sense of need and survival in the positive motivation dimension correlated negatively with educational levels of woman (r=-0.11, P=0.01); therefore, an increase in the educational level could decrease the childbearing motivation.

The findings were also indicative of a significant relationships between instrumental values of a child with husbands’ educational level (r=-0.14, P=0.001) and job (P= 0.003). Based on the obtained results, the highest fertility motivation for the instrumental values of a child was seen in lower educational level and unemployment of husbands. Parental stress in negative fertility motivations had a significant direct relationship with number of children (r=0.13, P=0.007); however, it had significant negative relationship with the level of income (r=-0.10, P=0.01).

There was a direct significant relationship between childcare challenges in the negative fertility motivation dimension and number of children (r=0.12, P=0.008), while there was a negative relationship with income level (r=-0.11, P=0.01). Childcare challenges crenelated significantly with job (P=0.013); therefore, the highest negative motivations for childcare challenges were seen in employed women.

The regression analysis method was used
to determine related factors to fertility motivations. In order to control effective variables, all variables and relevant variables (in correlation test) were considered as the independent variables. The two main variables of positive and negative fertility motivations were considered as dependent variables, which were separately included in the linear regression model. Finally, results indicated that studied variables predicted 12% of the variance of positive motivations (R2=0.12). Moreover, educational level and length of marriage were the strongest predictors (P <0.05). The studied variables also predicted 20% of variance of negative motivations (R2=0.20). As shown in table 4, among the investigated variables, income level, age, and number of children were the strongest predictors (P <0.05).

Discussion