Introduction
Marital adjustment is a evolutionary procedure in the relationship between couples that refers to a state of adjustment in various facets of their marriage where conflicts may arise [1, 2]. Marital incompatibility means dissatisfaction, lack of happiness, not agreeing on decisions and inappropriate communication. This problem destroys the life and has harmful effects on the physical and mental health of the couples. Moreover, this problem leads to a range of behavioral and emotional problems for children who are exposed to parental conflicts [3]. Including internalizing disorders, anxiety, depression, and withdrawal; externalizing disorders (aggression, delinquency, problems with adapting to the environment and friends, behavioral-emotional problems, educational and occupational problems, along with other problems such as alcohol and drug abuse, and suicide ( [4-7]. 
Understanding the individual characteristics of the spouse is an ongoing aspect of marriage because even if two people know each other before marriage or at the time of marriage, achieving marital adjustment necessitates maturity for acceptance and understanding by the spouse; without such maturity, marital discord becomes inevitable. Given the paramount importance of marital adjustment, it becomes essential to consider and evaluate this variable to enhance marital satisfaction [2, 8, 9].
A 2008 study across 27 countries reported an increasing prevalence of sexual dissatisfaction, and only 57% of men and 58% of women were satisfied with their sexual lives [10]. These data indicate that there are also drastic problems with marital adjustment, which is considered to be the barometer of sexual satisfaction [11, 12]. 
The structures of beliefs, attitudes, subjective norms, and enabling factors, which are called the BASNEF model, adapt two parent theories (PRECEDE model and theory of reasoned action) to the special needs of health education in developing [13, 14]. Therefore, the BASNEF model is one of the best for developing countries. Although various studies [15-26] were done in Iran and other countries on marital adjustment, no study has used the BASNEF model to determine the predictors of marital adjustment.
The collected data were subjected to descriptive and analytical analyses, including the Pearson correlation and structural equation modeling (SEM). The modeling allows for investigating the relationships between behavioral intention, enabling factors, and behavior, as posited in the conceptual framework of the BASNEF model. Model fit is assessed through the utilization of various fit indices, including the comparative fit index (CFI), Tucker-Lewis index (TLI), standardized root mean square (SRMR) and root mean square error of approximation (RMSEA). These discoveries offer implications for advancing targeted interventions focused on cultivating favorable health behaviors among women. By recognizing the fundamental factors influencing health behavior, medical professionals and policymakers can devise strategies to enhance knowledge, attitudes, subjective norms, and factors that positively impact behavioral intentions and health-related actions.
By focusing on a specific population and employing advanced statistical techniques, this study yields valuable insights that can inform targeted interventions and policies promoting positive health behavior. These findings contribute to the existing body of literature and have implications for healthcare providers, policymakers, and researchers striving to enhance health outcomes in similar contexts. Using the BASNEF model and quantitative analysis techniques, this research provides valuable insights into the determinants of marital adjustment within the context of comprehensive health service centers. So, the current study was designed to determine predictors of marital adjustment among females based on the BASNEF model.

Methods

Design and sample
This research is cross-sectional and was conducted from April to July 2023. The statistical population included women from the comprehensive health service centers of Torbat Heydarieh City, Iran, who have active health records. The research sample comprises 300 married women aged 18 to 49. After approving the plan and obtaining permission from the Ethics Committee, the list of all health centers in Torbat Heydarieh City was first obtained from the city health center. Then, sampling was done in two stages, a multi-stage cluster sampling. In this way, two centers were randomly selected from the comprehensive health centers of Torbat Heydariyeh. Next, the samples were included in the study through available sampling and proportional to the population of each center.
In SEM analysis, optimal and minimum sample sizes were 200 and 100, respectively [27]. More complex models would require even larger samples to achieve statistical power. This sample size of 300 seems sufficient for SEM.

Data collection and instrument
The data collection tool in this study was a questionnaire. The questionnaire consisted of demographic characteristics and researcher-made BASNEF model constructs. Demographic variables included age, education level, job, having a child, years of marriage, number of children, job and marriage age. BASNEF model constructs include 42 questions consisting of attitude (9 questions), subjective norms (9 questions), enabling factors (5 questions), behavioral intention (5 questions), knowledge (9 questions) and behavior (7 questions). It is worth noting that one of the project’s organizers was present at all stages of collecting participants’ information. For ethical considerations, informed consent was obtained from the participants’ husbands. The content validity method was used to determine the validity of this questionnaire. 
Thus, the questionnaire was first prepared based on books, articles and reliable sources. It was given to 10 experts, and the content validity index (CVI) and content validity ratio (CVR) were calculated. CVI and CVR of the questionnaire were 0.8 and 0.76, respectively.
The Cronbach α was used to determine the reliability of the questionnaire. Thus, the Cronbach α coefficient was found to be 0.79. This questionnaire includes knowledge questions. The correct answer was given a score of 3, and the wrong was given a score of 1. Attitude questions, behavioral intention, enabling factors, and subjective norms are scored on a 5-point Likert type as follows: Completely agree, score 4; agree, score 3; I have no opinion, score 2; I disagree, score 1; and completely disagree, score 0. Scoring in the reverse questions was as follows: Completely agree, score zero; agree, score 1; neither agreed nor disagreed, score 2; disagreed, score 3; and completely disagreed, score 4. It is worth mentioning that the questionnaire tool and its questions were presented in the form of face-to-face interviews with clear explanations of the study objectives for the participants by the researcher and relevant experts.
The inclusion criteria were full consent to participate in the research and complete the questionnaire, age range of 18-49 years, no history of divorce in the past year, living with a husband in the same house, the first marriage of a husband and wife, at least one year of cohabitation, and women referred for health care and had health records. The exclusion criteria were incomplete questionnaire, loss of a husband, death of a close relative in the last 6 months, pregnancy or childbirth in the previous 6 months, alcohol and drug use, and use of drugs related to mental disorders.

Statistical analysis
Data were analyzed using descriptive and analytical statistics, including the Pearson correlation and SEM. The modeling was performed on the total sample, based on the model hypothesized in Figure 1, to analyze how behavioral intention and enabling factors are related to behavior in women. The first stage of the process was the creation of the latent variables and hypothesizing the model, followed by confirmatory factor analysis (CFA), which was used to test the relationship between the latent variables.



SEM parameters were estimated, and a general fit of the model was assessed. The chi-square statistic value and the degrees of freedom are reported to evaluate the model’s fit. However, four other indices were considered because the chi-square was affected by sample size. Ideal goodness of fit was indicated by the CFI>0.9, TLI>0.9, SRMR<0.08 and RMSEA<0.08 [28].
The Mplus software, version 6 was applied to SEM. Descriptive and other analyses were performed using SPSS software, version 26. The significance level was set at 0.05.

Results
Based on the results taken from the questionnaires, demographic variables are presented in Table 1. Most women were between 30 and 40 years old (37%), had diplomas (39.3%), were housewives (69.1%), and had a child (87%). The mean years of marriage among samples was 12.94±8.43 years. 



The Spearman correlations showed a positive and significant correlation between behavioral intention (r=0.603, P<0.05), enabling factors (r=0.430, P<0.05), subjective norms (r=0.301, P<0.05) and attitude (r=0.0187, P<0.05) with behavior. 
The standardized factor loadings, as tabulated in Table 2, indicate how well the indicators explain or contribute towards their respective latent variable. As expected, all latent variable indicators showed significant factor loadings (P<0.05).




The values of standardized factor loadings to the standard error in Table 2 (ESTIMATE/S.E.) show that among the indicators of the knowledge factor, “questions 7 and 8,” among the indicators of the attitude factor, “questions 14 and 15,” among the indicators of the subjective norms factor, “questions 22, 23 and 24,” among the indicators of the enabling factors, “questions 29 and 31,” among the indicators of the behavioral intention factor, “questions 34 and 35,” among the indicators of the behavioral factor, “questions 40, 41 and 42” are the most effective predictive indicators that have been related to each factor.
At first, SEM was employed to analyze the factors affecting behavior based on the BASNEF model. Therefore, model 1 was extracted. However, some goodness of fit indices were not confirmed (χ2/df=2.56, CFI=0.712, TLI=0.695, RMSEA=0.072). In the next step, the goodness of fit indices of Model 2, which was the modified version of Model 1, was confirmed to obtain better goodness of fit index and determine the direct and indirect effects of constructs on each other and the dependent variable, i.e. behavior. The values of the fitness indices of model 2 are as follows: (χ2/df=2.20, CFI=0.78, TLI=0.77 and RMSEA=0.063). 
Figure 1 shows the direct and indirect effects of variables predicting behavior. 
As seen, behavioral intention (ρ=0.635, P<0.05) and enabling factors (ρ=0.335, P<0.05) are directly effective on behavior and the effect of behavioral intention is stronger compared to enabling factors. However, the enabling factors also indirectly affect the behavior by influencing the behavioral intention (ρ=0.300×0.653=0.196, P<0.05). Subjective norms indirectly influence behavior through behavioral intention (ρ=0.268×0.653=0.175, P<0.05). Knowledge (ρ=0.307×0.268=0.082, P<0.05) and attitude indirectly (ρ=0.530×0.268=0.142, P<0.05) affect behavioral intention through subjective norms.
Finally, the research findings indicated that the BASNEF constructs could predict 63% of the behavior variance.
Figure 2 shows a statistically significant relationship between education, spouse’s education, and job with marital adjustment (P<0.05).




Discussion
The present study was an exploratory study designed and implemented to determine the predictors of marital adjustment based on the constructs of the BASNEF model. The current study’s innovative aspect is using the BASNEF model for predicting marital adjustment. This novel approach provides new insight for studying marital adjustment and designing interventions based on the constructs of the BASNEF model. On the other hand, considering that no study has been done using the BASNEF model in this field, the current study is innovative, and in general, the BASNEF model is a valuable framework for predicting marital adjustment. 
Although no study has used the BASNEF model to determine predictors of marital adjustment or the effect of educational intervention based on the BASNEF model to promote marital adjustment, various studies have shown the impact of using the BASNEF model on couples’ interpersonal communication skills [29, 30] and the quality of sexual relationship, that both of them are similar subject and have a favorable effect on marital adjustment.
In the present study, there was a statistically significant relationship between education and marital adjustment consistent with the study of Quinn and Odell [16]. Still, it is inconsistent with Durgut and Kisa’s study [17]. The reasons for these contradictions are sociocultural dissimilarities and different understudied populations. Therefore, it is suggested that future studies be conducted in social and cultural groups with a larger sample size.
The present study showed a statistically significant relationship between a spouse’s education and marital adjustment, which is inconsistent with Durğut and Kisa’s study [17]. It seems that the reason for the contradictory results is the differences in the understudied population because Durğut and Kisa [17] studied those who had the experience of marriage under the age of 18. Therefore, it is suggested that more studies be conducted to investigate the relationship between a spouse’s education and marital adjustment and the mechanism of its effect.
The current study found a statistically significant relationship between job and marital adjustment, and the lowest marital adjustment was found in homemakers. Having a job with income increases marital adjustment and satisfaction because one can make decisions for various issues. On the other hand, less job stress leads to more marital adjustment [21].
The present study has several strengths. First, using a model, the predictors of marital adjustment were determined; therefore, it allows designing and implementing targeted interventions based on the model to increase marital adjustment. Secondly, due to the use of a model in this field, the present study is innovative and provides a new horizon for conducting studies on this issue. Thirdly, the results of the present study provide a practical guide for planners, policymakers, and counselors to promote marital adjustment and divorce prevention. Fourth, it should be mentioned that the BASNEF model is one of the best models for developing countries, so considering the lack of a similar study in this field, the findings of this study can play an essential role in improving marital adjustment and reducing its negative consequences.
The present study has several limitations. First, the study was conducted only on women referring to comprehensive health service centers, so caution should be taken in generalizing the results to all women. Secondly, due to the cultural characteristics of the understudied population and the nature of the study topic, the answers of the study subjects to the questions may not be accurate. Thirdly, the nature of the cross-sectional study limits the possibility of determining causal relationships.

Conclusion
Based on the present study’s findings, the BASNEF model constructs are suitable predictors for marital adjustment. Also, a significant relationship exists between education, spouse’s education, and job with marital adjustment. Therefore, it is suggested that educational interventions be designed and implemented based on the BASNEF model. Also, interventions should be done to emphasize women whose education and their husband’s education are lower and homemakers. Considering sociocultural factors and differences in study populations, we recommended designing studies based on the BASNEF model in different populations in future research.

Ethical Considerations

Compliance with ethical guidelines
This study was approved by the Ethics Committee of Tehran University of Medical Sciences, Tehran, Iran (Code: IR.THUMS.REC.1402.004). All participants entered the study with informed consent. The questionnaires were anonymous, and the researchers kept the participants’ information confidential. 

Funding
This study was supported by Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.

Authors' contributions
Study design: Hadi Alizadeh Siuki and Hashem Heshmati; Data collection: Adele Ramzani; Data analysis, interpretation and preparing the manuscript: All authors.

Conflict of interest
The authors declared no conflict of interest.

Acknowledgments
The authors thank all participants in the study.




References