Predicting breast cancer screening behavior based on childhood and adolescent behavioral inhibition and risk perception in women over 20 years old

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Behavioral inhibition and risk perception are two psychological factors that may influence screening behaviors. However, few studies have specifically examined the combined impact of behavioral inhibition and risk perception. This study aims to address this gap by elucidating the psychological barriers to breast cancer screening. Methods This descriptive-analytical, cross-sectional study was conducted in 2024 among women aged 20 and older in the city of Qom. Cluster sampling was employed, resulting in a sample of 315 participants. The standardized questionnaire was used consisted of four sections: demographic information, behavioral inhibition, breast cancer risk perception, and screening behavior. The tools used demonstrated adequate validity and reliability, and the data were analyzed using SPSS software. Statistical tests, including correlation coefficients and logistic regression, were utilized to explore the relationships among the variables. Results The mean age of the participants was 34.98 ± 9.41 years. Pearson correlation coefficient analysis indicated a significant relationship between behavior and components of behavioral inhibition (p < 0.001, r = 0.259) and risk perception (p = 0.033, r = -0.120). Furthermore, the regression analysis revealed that both behavioral inhibition and risk perception could predict screening behavior (p < 0.05), with risk perception having a greater contribution to the prediction of behavior. Conclusion The present study indicates that behavioral inhibition acts as a barrier while risk perception serves as a facilitating factor, which can guide therapeutic and psychological interventions. Cancer Perception Risk Behavioral Research Psychological Inhibition Introduction Breast cancer remains one of the most prevalent cancers among women worldwide, with late diagnosis leading to poor treatment outcomes and low survival rates ( 1 ). Alarmingly, statistics indicate that every minute, four women are diagnosed with breast cancer globally, and one woman succumbs to the disease. On average, one in every twenty women will be diagnosed with breast cancer at some point in their lives ( 2 ). These figures highlight a significant global health issue with far-reaching implications for health systems, healthcare providers, and the quality of life of patients. Early detection through regular breast cancer screening plays a crucial role in improving these outcomes ( 3 ). Mammography, ultrasound, MRI, and clinical breast examinations represent a range of screening methods whose effectiveness varies based on factors such as age, risk factors, and breast tissue density ( 4 ). When cancers are diagnosed at early stages, treatment tends to be less invasive and more likely to succeed, contributing to increased survival rates and better health outcomes. However, studies indicate that many women, especially younger women, do not engage in recommended screening behaviors ( 5 ). For instance, studies conducted in Iran have shown that only 20 percent of young women participate in screening programs ( 6 ). This gap between guidelines and actual participation raises important questions regarding barriers to engagement and how to address these issues across various cultural and healthcare contexts. Factors such as perceived sensitivity, perceived barriers, and health system-related issues (like accessibility, cost, and service availability) shape participation rates ( 7 ). Furthermore, fear, anxiety, and a lack of understanding regarding risks have been identified as significant barriers, raising critical questions about the underlying psychological factors that influence women's health choices ( 8 , 9 ). Previous studies have demonstrated a positive correlation between psychosocial determinants and the intention and behavior related to screening across diverse populations ( 10 , 11 ). Behavioral inhibition and risk perception are also two psychological factors that may impact participation in health-promoting behaviors such as breast cancer screening ( 12 ). Behavioral inhibition, often characterized by a tendency to withdraw from potentially threatening situations, may lead to avoidance of medical interventions ( 13 ). Functionally, behavioral inhibition has been associated with heightened anticipated anxiety and avoidant coping, which can manifest as delays or refusals to undergo screening ( 14 ). Additionally, risk perception may play a pivotal role in the likelihood of engaging in screening methods ( 15 ). Individuals who overestimate the risks associated with screening procedures or become preoccupied with false-positive results may experience heightened anxiety that prevents them from pursuing screening ( 16 ). Consequently, these constructs may shape decision-making processes and influence an individual's feelings about their health status. Despite these insights, the nuanced relationship between these psychological constructs and breast cancer screening has not been sufficiently explored ( 17 ). Furthermore, evidence suggests that early life experiences significantly shape future health behaviors ( 18 ). Research conducted by Daines CL et al. (2021) indicates that childhood and adolescent experiences significantly influence adult health behaviors ( 19 ). However, previous studies have primarily focused on adult years, with less attention given to the formative childhood and adolescent experiences that may influence health behaviors later in life ( 20 , 21 ). Behavioral inhibition developed during childhood is often linked to adult anxiety. According to a study by Alex J et al. (2014), breast cancer screening, particularly mammography, can be anxiety-inducing for some women, leading to avoidance of screening methods ( 13 , 22 ). While existing research has identified risk perception as a significant factor influencing breast cancer screening behaviors, few studies have specifically examined the combined effects of behavioral inhibition and risk perception on breast cancer screening ( 15 , 17 ). The findings of this study will contribute to the existing body of knowledge and provide valuable insights into effective strategies for enhancing breast cancer screening compliance among women. This study aims to address this gap by determining and predicting breast cancer screening behavior based on childhood and adolescent behavioral inhibition and risk perception in women over the age of 20. This research seeks to clarify the psychological barriers that may hinder participation in screening programs. Methods The present study is a descriptive-analytical and cross-sectional research conducted in 2024 among women over 20 years old attending healthcare centers in Qom. A multi-stage cluster sampling method was employed. In the first stage, eight healthcare centers were selected randomly from the eight urban districts of Qom. Subsequently, a list of eligible women was extracted from each center, and samples were selected randomly based on the inclusion criteria. The sample size was calculated using the formula for estimating sample size in cross-sectional studies, considering a Type I error rate of 0.05, the variance of breast cancer screening behavior according to the study by Rakhshani et al., and a precision level of 0.4. This resulted in an estimated sample size of 315 participants ( 23 ). Inclusion criteria for the study were being over 20 years of age, providing written informed consent to participate, and the absence of a breast cancer diagnosis. Women with a history of chronic illnesses, mental disorders, or a personal or first-degree relative history of breast cancer were excluded from the study. The data collection instrument was a standardized questionnaire that comprised four sections: The first section of the questionnaire included demographic characteristics consisting of six questions, covering age, marital status, education, income, occupation, and access to health services. The second part of the questionnaire assessed behavioral inhibition through 16 questions using the Behavioral Inhibition Scale developed by Goldstone and Parker ( 24 ). The aim was to measure childhood behavioral inhibition (prior to the age of 13) in adults. It employed a five-point Likert scale (No = 0, Rarely = 1, Sometimes = 2, Often = 3, Almost Always = 4). To obtain the total score of the questionnaire, the scores of all questions were summed. Higher scores on this scale indicated greater levels of childhood behavioral inhibition in the respondent, and vice versa. Internal consistency for this tool was reported at 0.90 using Cronbach's alpha in the study by Goldstone and Parker, while its reliability was 0.86 through test-retest methods. Mohammadi (2007) reported an internal consistency of 0.74 for this tool in a sample of 400 Iranian students, with a convergent validity coefficient of 0.55 when correlated with the Adult Behavioral Inhibition Scale, and a reliability of 0.71 using a two-week test-retest interval ( 25 ). The third section encompassed 10 questions assessing risk perception employing the Breast Cancer Perception Scale developed by Thailand et al. ( 26 ). Its aim was to measure perceived social stigma, perceived fear, and perceived risk of breast cancer. Responses were rated on a five-point Likert scale from Strongly Disagree ( 1 ) to Strongly Agree ( 5 ). The total score was calculated by summing the responses, with higher scores indicating greater risk perception on the part of the respondent. Internal consistency for this scale, as reported by Thailand et al., ranged from 0.815 to 0.950 using Cronbach's alpha. Meshayekh Amiri and colleagues (2023) reported an overall internal consistency of 0.68 for this tool in a sample of 327 Iranian women, with CVI and CVR scores determined at 0.98 and 0.95, respectively, and a reliability of 0.97 using a two-week test-retest method ( 27 ). The final section of the questionnaire comprised three questions, utilizing the Breast Cancer Screening Behavior Scale based on the PEN-3 model by Naghibi et al. (2016) ( 28 ), whose validity and reliability were assessed and confirmed in their study. The objective was to measure performance in utilizing early detection methods for breast cancer among women. Responses were given as Yes, No, with scores of 0 and 1. Total scores were calculated by summing all question responses, with higher scores indicating better performance in breast cancer screening. The validity of the scale was established through content validity assessment in Naghibi et al.'s study. The reliability of the questionnaire was reported at 0.80 using Cronbach’s alpha for the performance section of screening. Statistical Analysis The collected data were analyzed using SPSS software version 23. Statistical tests, including Pearson correlation and logistic regression, were used to examine relationships between variables. Ethical Considerations Participation in the study was voluntary. Before distributing the questionnaire, the study's purpose was explained to the target group; informed written consent was obtained from the participants, and completed questionnaires were collected anonymously while adhering to data confidentiality principles. This study was approved by the Research Ethics Committee of Qom University of Medical Sciences with the code IR.MUQ.REC.1401.210. Results The assessment of the study participants revealed that the average age of the participants was 34.98 years, with a standard deviation of ± 9.41. Based on the findings presented in Table 1 , the majority of the participants had a university education (40.6%) and were homemakers (57.8%). Table 1 Demographic Characteristics of Participants Variable Number Percentage Income Low (less than 10 million tomans) 152 48.3 Medium (between 10 million and 20 million tomans) 137 43.5 High(more than 20 million tomans) 26 8.3 Education Illiterate 11 3.5 Primary 42 13.3 Secondary 52 16.5 Diploma 82 26.0 University 128 40.6 Marital Status Single 56 17.8 Married 236 74.9 Widowed 7 2.2 Divorced 16 5.1 Occupation Self-employed 32 10.2 Employee 63 20.0 Homemaker 182 57.8 Other 38 12.1 Access to Health Services Yes 270 85.7% No 45 14.3% The results of the study indicated that the mean score for behavioral variables was 31.05 ± 9.86, while the mean score for behavioral inhibition was 30.98 ± 8.39. Additionally, the mean score for risk perception was 30.63 ± 7.02. Other measures of dispersion for the variables are displayed in Table 2 . Table 2 Mean Values and Standard Deviations for Behavioral Inhibition and Risk Perception Components Minimum Maximum Mean Standard Deviation Range Behavioral Inhibition 7 54 30.98 8.39 0–64 Risk Perception 10 50 30.63 7.02 10–50 In examining the relationship between demographic variables, Pearson correlation coefficient analysis revealed a significant correlation between age and behavioral variables (p < 0.001; r = 0.371) and risk perception (p < 0.001; r = 0.191). However, no significant relationship was observed between age and behavioral inhibition (p = 0.895; r = -0.007). Furthermore, Pearson correlation coefficient analysis indicated a significant relationship between behavior and both behavioral inhibition (p < 0.001; r = 0.259) and risk perception (p = 0.033; r = -0.120) (Table 3 ). Table 3 Correlation Coefficients and Significance Levels between Behavior, Behavioral Inhibition, and Risk Perception Behavior Behavioral Inhibition Risk Perception P 0.000 0.033 r 0.259 (**) -0.120 (*) The results from Table 4 indicate that both behavioral inhibition and risk perception were able to predict behavior (p < 0.05). With a confidence level of 95%, the contribution of behavioral inhibition was 11.2%, and the contribution of risk perception was 25.5%. Thus, it can be confidently stated that risk perception plays the most significant role in predicting behavior. Table 4 Multiple Regression Analysis of Behavioral Inhibition and Risk Perception Variables Model Unstandardized Coefficients Standardized Coefficients t Significance Level B Std. Error Beta Constant 0.429 0.344 1.24 0.213 Behavioral Inhibition -0.01 0.007 -0.112 -2.05 Risk Perception 0.03 0.008 0.255 4.69 Discussion The findings of the present study indicate a significant negative relationship between behavioral inhibition and breast cancer screening behavior. This suggests that women with higher levels of behavioral inhibition are less likely to engage in screening due to increased anxiety and worry ( 29 ). This aligns with findings from a study conducted by Almutairi et al. (2025), which showed that physical, psychological, and social factors influence the level of anxiety in women undergoing mammography screening, potentially explaining the low adherence to mammography in Saudi Arabia ( 30 ). Additionally, Verhagen et al. (2023) reported that behavioral inhibition during adolescence is associated with increased anxiety and social withdrawal in adulthood, which may explain why women avoid confronting breast cancer screening methods ( 13 ). A study by Rakhshani et al. (2018) in Iran also demonstrated that anxiety and fear of a cancer diagnosis are among the primary reasons women refrain from screening ( 23 ). However, some studies have presented fear and anxiety as dual factors, indicating that under certain conditions, they can serve as barriers, while in other situations, they may act as motivators for screening behavior ( 31 ). The lack of direct measurement of behavioral inhibition in breast cancer screening studies is evident. No study in this area has utilized specific scales designed to assess behavioral inhibition (e.g., the Adult Behavioral Inhibition Scale) ( 22 ). This represents a substantial gap, as the construct of behavioral inhibition significantly differs from related but distinct constructs such as fear and anxiety ( 32 ). Conversely, risk perception was found to have a positive and significant relationship with breast cancer screening behavior in the current study. Additionally, regression analysis indicated that both behavioral inhibition and risk perception could predict screening behavior, with risk perception playing a greater role in this prediction. These findings are consistent with various studies. Research by Zahedi et al. (2021) in Iran demonstrated that women with a higher level of risk perception were more likely to participate in screening programs ( 12 ). Moreover, a study by Ho et al. (2025) corroborated the current findings, revealing that increasing awareness and providing individualized predictions of breast cancer risk could enhance risk perception and consequently increase participation in screening ( 15 ). Conversely, a study by De Pelsmacker et al. (2017) in Norway indicated that higher risk perception led to lower attendance and participation of women in population-based free breast cancer screening programs, which contrasts with the findings of the present study ( 33 ). Conclusion Overall, the results of this research indicate that behavioral inhibition can act as a hindrance, while risk perception can serve as a facilitating factor in breast cancer screening behavior. These findings underscore the importance of addressing psychological dimensions alongside educational and informational interventions in women's health promotion programs. Practically, the findings of the current study can guide educational and psychological interventions. Counseling programs aimed at reducing anxiety and fear in women with high levels of behavioral inhibition, combined with targeted training to enhance risk perception, may lead to increased participation rates in screening. Despite this, the current study has its limitations. Firstly, due to the cross-sectional nature of the research, causal relationships cannot be inferred. Secondly, the data were collected based on self-reported questionnaires, which increases the likelihood of bias. Therefore, it is recommended that future research employ longitudinal and interventional designs to also investigate the effects of educational and psychological interventions on reducing behavioral inhibition and enhancing risk perception. Declarations Competing interests Amin Arabshahi : Conceptualization, study design, literature search, and drafting of the introduction and discussion sections. Kimiya Namazi Data collection, article screening, and contribution to writing the Materials and Methods section. Maryam Ahmadi Data analysis, organization of results, and scientific editing of the manuscript. Zabihollah Gharlipour Overall supervision of the project, final approval of the manuscript, and responsibility for correspondence with the journal. Ethics approval and consent to participate This study was approved by the Research Ethics Committee of Qom University of Medical Sciences (Approval Code: IR.MUQ.REC.1401.210). Participation in the study was voluntary. Prior to data collection, the objectives of the study were explained to all participants, and written informed consent was obtained. All questionnaires were completed anonymously, and confidentiality of data was strictly maintained. Financial support and sponsorship The current research was funded by the Vice Chancellor for Research of Qom’s University of Medical Sciences. Conflicts of interest No item has been mentioned by the authors. Funding The study was conducted as part of a research plan approved by the Research and Technology Deputy of Qom Author Contribution Amin Arabshahi: Conceptualization, study design, literature search, and drafting of the introduction and discussion sections.Kimiya Namazi : Data collection, article screening, and contribution to writing the Materials and Methods section.Maryam Ahmadi : Data analysis, organization of results, and scientific editing of the manuscript.Zabihollah Gharlipour: Overall supervision of the project, final approval of the manuscript, and responsibility for correspondence with the journal. Acknowledgement The authors wish to thank the study participants and the Research Development Unit of Qom University of Medical Sciences, as well as the health and healthcare centers in Qom. Data Availability All data that support the findings of this study are included in this manuscript and its supplementary information files. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8936158","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599533997,"identity":"f4ec259d-d46e-4426-b045-dfc5b6891529","order_by":0,"name":"Amin Arabshahi","email":"","orcid":"","institution":"Tarbiat Modares, University,","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"","lastName":"Arabshahi","suffix":""},{"id":599533998,"identity":"04e79e6d-05b5-4eb1-9ee2-3b3446212007","order_by":1,"name":"Kimiya Namazi","email":"","orcid":"","institution":"Qom University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kimiya","middleName":"","lastName":"Namazi","suffix":""},{"id":599533999,"identity":"70593380-3baf-40ff-b8a9-3a90c59ee4ec","order_by":2,"name":"Maryam Ahmadi","email":"","orcid":"","institution":"Qom University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Ahmadi","suffix":""},{"id":599534000,"identity":"8d290a64-daa1-4610-95a5-cda30f4a4dcc","order_by":3,"name":"Dr. Zabiullah Gharlipour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCSB+YABmMj5gYDhApJYEiBZmAxK0QJhsEkRpkY9ufvYhoeCOnMHt48+qeWruyPEzMD98dAOPFsM7x4xnJBg8MzY4l5B2m+fYM2PJBjZj4xx8WmYkGAP9cjhxwxmGY7d52ICMAzxs0vi1pH+GamFsK+b5R4QWeYkcmC3MbMy8bURoMZDIKWYA+UXyDBuz5Ny+w8aSzQT8Ij8jfTPDhz935PjOsD/88ObbYTl+9uaHj/HacgBMQUgmHhDJjEc52JYGJC2MPwioHgWjYBSMgpEJAJJiUDj+ZQYhAAAAAElFTkSuQmCC","orcid":"","institution":"Qom University of Medical Sciences","correspondingAuthor":true,"prefix":"Dr.","firstName":"Zabiullah","middleName":"","lastName":"Gharlipour","suffix":""}],"badges":[],"createdAt":"2026-02-22 00:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8936158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8936158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104401565,"identity":"2adb794b-7c07-447a-b433-df00d01b1226","added_by":"auto","created_at":"2026-03-11 12:13:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":686667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8936158/v1/b9823c3e-465f-49f9-96df-ed8ead1073a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting breast cancer screening behavior based on childhood and adolescent behavioral inhibition and risk perception in women over 20 years old","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer remains one of the most prevalent cancers among women worldwide, with late diagnosis leading to poor treatment outcomes and low survival rates (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Alarmingly, statistics indicate that every minute, four women are diagnosed with breast cancer globally, and one woman succumbs to the disease. On average, one in every twenty women will be diagnosed with breast cancer at some point in their lives (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These figures highlight a significant global health issue with far-reaching implications for health systems, healthcare providers, and the quality of life of patients. Early detection through regular breast cancer screening plays a crucial role in improving these outcomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Mammography, ultrasound, MRI, and clinical breast examinations represent a range of screening methods whose effectiveness varies based on factors such as age, risk factors, and breast tissue density (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). When cancers are diagnosed at early stages, treatment tends to be less invasive and more likely to succeed, contributing to increased survival rates and better health outcomes. However, studies indicate that many women, especially younger women, do not engage in recommended screening behaviors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For instance, studies conducted in Iran have shown that only 20 percent of young women participate in screening programs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This gap between guidelines and actual participation raises important questions regarding barriers to engagement and how to address these issues across various cultural and healthcare contexts. Factors such as perceived sensitivity, perceived barriers, and health system-related issues (like accessibility, cost, and service availability) shape participation rates (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, fear, anxiety, and a lack of understanding regarding risks have been identified as significant barriers, raising critical questions about the underlying psychological factors that influence women's health choices (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Previous studies have demonstrated a positive correlation between psychosocial determinants and the intention and behavior related to screening across diverse populations (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Behavioral inhibition and risk perception are also two psychological factors that may impact participation in health-promoting behaviors such as breast cancer screening (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Behavioral inhibition, often characterized by a tendency to withdraw from potentially threatening situations, may lead to avoidance of medical interventions (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Functionally, behavioral inhibition has been associated with heightened anticipated anxiety and avoidant coping, which can manifest as delays or refusals to undergo screening (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, risk perception may play a pivotal role in the likelihood of engaging in screening methods (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Individuals who overestimate the risks associated with screening procedures or become preoccupied with false-positive results may experience heightened anxiety that prevents them from pursuing screening (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Consequently, these constructs may shape decision-making processes and influence an individual's feelings about their health status. Despite these insights, the nuanced relationship between these psychological constructs and breast cancer screening has not been sufficiently explored (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, evidence suggests that early life experiences significantly shape future health behaviors (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Research conducted by Daines CL et al. (2021) indicates that childhood and adolescent experiences significantly influence adult health behaviors (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, previous studies have primarily focused on adult years, with less attention given to the formative childhood and adolescent experiences that may influence health behaviors later in life (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Behavioral inhibition developed during childhood is often linked to adult anxiety. According to a study by Alex J et al. (2014), breast cancer screening, particularly mammography, can be anxiety-inducing for some women, leading to avoidance of screening methods (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). While existing research has identified risk perception as a significant factor influencing breast cancer screening behaviors, few studies have specifically examined the combined effects of behavioral inhibition and risk perception on breast cancer screening (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of this study will contribute to the existing body of knowledge and provide valuable insights into effective strategies for enhancing breast cancer screening compliance among women. This study aims to address this gap by determining and predicting breast cancer screening behavior based on childhood and adolescent behavioral inhibition and risk perception in women over the age of 20. This research seeks to clarify the psychological barriers that may hinder participation in screening programs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe present study is a descriptive-analytical and cross-sectional research conducted in 2024 among women over 20 years old attending healthcare centers in Qom. A multi-stage cluster sampling method was employed. In the first stage, eight healthcare centers were selected randomly from the eight urban districts of Qom. Subsequently, a list of eligible women was extracted from each center, and samples were selected randomly based on the inclusion criteria.\u003c/p\u003e \u003cp\u003eThe sample size was calculated using the formula for estimating sample size in cross-sectional studies, considering a Type I error rate of 0.05, the variance of breast cancer screening behavior according to the study by Rakhshani et al., and a precision level of 0.4. This resulted in an estimated sample size of 315 participants (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInclusion criteria for the study were being over 20 years of age, providing written informed consent to participate, and the absence of a breast cancer diagnosis. Women with a history of chronic illnesses, mental disorders, or a personal or first-degree relative history of breast cancer were excluded from the study.\u003c/p\u003e \u003cp\u003eThe data collection instrument was a standardized questionnaire that comprised four sections:\u003c/p\u003e \u003cp\u003eThe first section of the questionnaire included demographic characteristics consisting of six questions, covering age, marital status, education, income, occupation, and access to health services.\u003c/p\u003e \u003cp\u003eThe second part of the questionnaire assessed behavioral inhibition through 16 questions using the Behavioral Inhibition Scale developed by Goldstone and Parker (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The aim was to measure childhood behavioral inhibition (prior to the age of 13) in adults. It employed a five-point Likert scale (No\u0026thinsp;=\u0026thinsp;0, Rarely\u0026thinsp;=\u0026thinsp;1, Sometimes\u0026thinsp;=\u0026thinsp;2, Often\u0026thinsp;=\u0026thinsp;3, Almost Always\u0026thinsp;=\u0026thinsp;4). To obtain the total score of the questionnaire, the scores of all questions were summed. Higher scores on this scale indicated greater levels of childhood behavioral inhibition in the respondent, and vice versa. Internal consistency for this tool was reported at 0.90 using Cronbach's alpha in the study by Goldstone and Parker, while its reliability was 0.86 through test-retest methods. Mohammadi (2007) reported an internal consistency of 0.74 for this tool in a sample of 400 Iranian students, with a convergent validity coefficient of 0.55 when correlated with the Adult Behavioral Inhibition Scale, and a reliability of 0.71 using a two-week test-retest interval (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe third section encompassed 10 questions assessing risk perception employing the Breast Cancer Perception Scale developed by Thailand et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Its aim was to measure perceived social stigma, perceived fear, and perceived risk of breast cancer. Responses were rated on a five-point Likert scale from Strongly Disagree (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to Strongly Agree (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The total score was calculated by summing the responses, with higher scores indicating greater risk perception on the part of the respondent. Internal consistency for this scale, as reported by Thailand et al., ranged from 0.815 to 0.950 using Cronbach's alpha. Meshayekh Amiri and colleagues (2023) reported an overall internal consistency of 0.68 for this tool in a sample of 327 Iranian women, with CVI and CVR scores determined at 0.98 and 0.95, respectively, and a reliability of 0.97 using a two-week test-retest method (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe final section of the questionnaire comprised three questions, utilizing the Breast Cancer Screening Behavior Scale based on the PEN-3 model by Naghibi et al. (2016) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), whose validity and reliability were assessed and confirmed in their study. The objective was to measure performance in utilizing early detection methods for breast cancer among women. Responses were given as Yes, No, with scores of 0 and 1. Total scores were calculated by summing all question responses, with higher scores indicating better performance in breast cancer screening. The validity of the scale was established through content validity assessment in Naghibi et al.'s study. The reliability of the questionnaire was reported at 0.80 using Cronbach\u0026rsquo;s alpha for the performance section of screening.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe collected data were analyzed using SPSS software version 23. Statistical tests, including Pearson correlation and logistic regression, were used to examine relationships between variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eParticipation in the study was voluntary. Before distributing the questionnaire, the study's purpose was explained to the target group; informed written consent was obtained from the participants, and completed questionnaires were collected anonymously while adhering to data confidentiality principles. This study was approved by the Research Ethics Committee of Qom University of Medical Sciences with the code IR.MUQ.REC.1401.210.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe assessment of the study participants revealed that the average age of the participants was 34.98 years, with a standard deviation of \u0026plusmn;\u0026thinsp;9.41. Based on the findings presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the majority of the participants had a university education (40.6%) and were homemakers (57.8%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (less than 10\u0026nbsp;million tomans)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (between 10\u0026nbsp;million and 20\u0026nbsp;million tomans)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh(more than 20\u0026nbsp;million tomans)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccess to Health Services\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the study indicated that the mean score for behavioral variables was 31.05\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86, while the mean score for behavioral inhibition was 30.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.39. Additionally, the mean score for risk perception was 30.63\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02. Other measures of dispersion for the variables are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean Values and Standard Deviations for Behavioral Inhibition and Risk Perception\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBehavioral Inhibition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk Perception\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn examining the relationship between demographic variables, Pearson correlation coefficient analysis revealed a significant correlation between age and behavioral variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.371) and risk perception (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.191). However, no significant relationship was observed between age and behavioral inhibition (p\u0026thinsp;=\u0026thinsp;0.895; r = -0.007). Furthermore, Pearson correlation coefficient analysis indicated a significant relationship between behavior and both behavioral inhibition (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.259) and risk perception (p\u0026thinsp;=\u0026thinsp;0.033; r = -0.120) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Coefficients and Significance Levels between Behavior, Behavioral Inhibition, and Risk Perception\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral Inhibition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Perception\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.259 (**)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.120 (*)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that both behavioral inhibition and risk perception were able to predict behavior (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). With a confidence level of 95%, the contribution of behavioral inhibition was 11.2%, and the contribution of risk perception was 25.5%. Thus, it can be confidently stated that risk perception plays the most significant role in predicting behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Regression Analysis of Behavioral Inhibition and Risk Perception Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificance Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBehavioral Inhibition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk Perception\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of the present study indicate a significant negative relationship between behavioral inhibition and breast cancer screening behavior. This suggests that women with higher levels of behavioral inhibition are less likely to engage in screening due to increased anxiety and worry (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This aligns with findings from a study conducted by Almutairi et al. (2025), which showed that physical, psychological, and social factors influence the level of anxiety in women undergoing mammography screening, potentially explaining the low adherence to mammography in Saudi Arabia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Additionally, Verhagen et al. (2023) reported that behavioral inhibition during adolescence is associated with increased anxiety and social withdrawal in adulthood, which may explain why women avoid confronting breast cancer screening methods (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). A study by Rakhshani et al. (2018) in Iran also demonstrated that anxiety and fear of a cancer diagnosis are among the primary reasons women refrain from screening (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, some studies have presented fear and anxiety as dual factors, indicating that under certain conditions, they can serve as barriers, while in other situations, they may act as motivators for screening behavior (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The lack of direct measurement of behavioral inhibition in breast cancer screening studies is evident. No study in this area has utilized specific scales designed to assess behavioral inhibition (e.g., the Adult Behavioral Inhibition Scale) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This represents a substantial gap, as the construct of behavioral inhibition significantly differs from related but distinct constructs such as fear and anxiety (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, risk perception was found to have a positive and significant relationship with breast cancer screening behavior in the current study. Additionally, regression analysis indicated that both behavioral inhibition and risk perception could predict screening behavior, with risk perception playing a greater role in this prediction. These findings are consistent with various studies. Research by Zahedi et al. (2021) in Iran demonstrated that women with a higher level of risk perception were more likely to participate in screening programs (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Moreover, a study by Ho et al. (2025) corroborated the current findings, revealing that increasing awareness and providing individualized predictions of breast cancer risk could enhance risk perception and consequently increase participation in screening (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Conversely, a study by De Pelsmacker et al. (2017) in Norway indicated that higher risk perception led to lower attendance and participation of women in population-based free breast cancer screening programs, which contrasts with the findings of the present study (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, the results of this research indicate that behavioral inhibition can act as a hindrance, while risk perception can serve as a facilitating factor in breast cancer screening behavior. These findings underscore the importance of addressing psychological dimensions alongside educational and informational interventions in women's health promotion programs. Practically, the findings of the current study can guide educational and psychological interventions. Counseling programs aimed at reducing anxiety and fear in women with high levels of behavioral inhibition, combined with targeted training to enhance risk perception, may lead to increased participation rates in screening.\u003c/p\u003e \u003cp\u003eDespite this, the current study has its limitations. Firstly, due to the cross-sectional nature of the research, causal relationships cannot be inferred. Secondly, the data were collected based on self-reported questionnaires, which increases the likelihood of bias. Therefore, it is recommended that future research employ longitudinal and interventional designs to also investigate the effects of educational and psychological interventions on reducing behavioral inhibition and enhancing risk perception.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003e \u003cb\u003eAmin Arabshahi\u003c/b\u003e: Conceptualization, study design, literature search, and drafting of the introduction and discussion sections.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKimiya Namazi\u003c/strong\u003e \u003cp\u003eData collection, article screening, and contribution to writing the Materials and Methods section.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMaryam Ahmadi\u003c/strong\u003e \u003cp\u003eData analysis, organization of results, and scientific editing of the manuscript.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eZabihollah Gharlipour\u003c/strong\u003e \u003cp\u003eOverall supervision of the project, final approval of the manuscript, and responsibility for correspondence with the journal.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was approved by the Research Ethics Committee of Qom University of Medical Sciences (Approval Code: IR.MUQ.REC.1401.210). Participation in the study was voluntary. Prior to data collection, the objectives of the study were explained to all participants, and written informed consent was obtained. All questionnaires were completed anonymously, and confidentiality of data was strictly maintained.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFinancial support and sponsorship\u003c/h2\u003e \u003cp\u003eThe current research was funded by the Vice Chancellor for Research of Qom\u0026rsquo;s University of Medical Sciences.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eNo item has been mentioned by the authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was conducted as part of a research plan approved by the Research and Technology Deputy of Qom\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAmin Arabshahi: Conceptualization, study design, literature search, and drafting of the introduction and discussion sections.Kimiya Namazi : Data collection, article screening, and contribution to writing the Materials and Methods section.Maryam Ahmadi : Data analysis, organization of results, and scientific editing of the manuscript.Zabihollah Gharlipour: Overall supervision of the project, final approval of the manuscript, and responsibility for correspondence with the journal.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors wish to thank the study participants and the Research Development Unit of Qom University of Medical Sciences, as well as the health and healthcare centers in Qom.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data that support the findings of this study are included in this manuscript and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan der Veer EL, Lameijer J, Coolen AM, Bluekens AM, Nederend J, Gielens M, et al. Causes and consequences of delayed diagnosis in breast cancer screening with a focus on mammographic features and tumour characteristics. Eur J Radiol. 2023;167:111048.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Harper A, McCormack V, Sung H, Houssami N, Morgan E et al. Global patterns and trends in breast cancer incidence and mortality across 185 countries. Nat Med. 2025:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAboagye SO, Hunt JA, Ball G, Wei Y. Portable noninvasive technologies for early breast cancer detection: A systematic review. Comput Biol Med. 2024;182:109219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatsika L, Boureka E, Kalogiannidis I, Tsakiridis I, Tirodimos I, Lallas K et al. Screening for Breast Cancer: A Comparative Review of Guidelines. Life (Basel). 2024;14(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham QT, Hac HM, Dang HY, Lee CF, Kwok C, Le TC, et al. editors. Breast Cancer Screening Practices Among Women in Low-and Middle-Income Countries: A Perspective from Vietnam. Seminars in Oncology Nursing. Elsevier; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDehkordi PR, Dolatshahi Z, Gorji HA, Hashemi SM, Reisi N, Khalilabad TH. A Scoping Review of 20 Years Breast Cancer Screening Programs in Iran. Iran J Public Health. 2025;54(1):88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavakoli B, Feizi A, Zamani-Alavijeh F, Shahnazi H. Factors influencing breast cancer screening practices among women worldwide: a systematic review of observational and qualitative studies. BMC Womens Health. 2024;24(1):268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeed S, Asim M, Sohail MM. Fears and barriers: problems in breast cancer diagnosis and treatment in Pakistan. BMC Womens Health. 2021;21:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvanova A, Kvalem IL. Psychological predictors of intention and avoidance of attending organized mammography screening in Norway: applying the Extended Parallel Process Model. BMC Womens Health. 2021;21:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Pan Y, Li Q. Influencing factors of health screening among retirees: an extended TPB approach. Front Public Health. 2024;Volume 12\u0026ndash;2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajek A, Bock J-O, K\u0026ouml;nig H-H. The role of general psychosocial factors for the use of cancer screening\u0026mdash;Findings of a population-based observational study among older adults in Germany. Cancer Med. 2017;6(12):3025\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahedi R, Rezapour M, Molavi Vardanjani H, Baneshi MR, Haghdoost AA, Malekpour Afshar R, et al. Breast cancer risk perception and screening behaviors of Iranian Women. Women\u0026rsquo;s Health Bull. 2021;8(2):98\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerhagen M, Derks M, Roelofs K, Maciejewski D. Behavioral inhibition, negative parenting, and social withdrawal: Longitudinal associations with loneliness during early, middle, and late adolescence. Child Dev. 2023;94(2):512\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkumsnes T, Fjermestad KW, Wergeland GJ, Aalberg M, Heiervang ER, Kodal A, et al. Behavioral Inhibition and Social Anxiety Disorder as Predictors of Long-Term Outcomes of Cognitive Behavioral Therapy for Youth Anxiety Disorders. 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Iran J Psychiatry Clin Psychol. 2009;15(3):274\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylan S, \u0026Ouml;zkan İ, Adıbelli D. Breast cancer perception scale: Psychometric development study. Eur J Breast Health. 2021;17(2):95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMashayekh-Amiri S, Jafarabadi MA, Hosseinzadeh M, Kanani Es, Mirghafourvand M. Measurement properties of the Iranian version of the breast cancer perception scale (BCPS) according to the COSMIN checklist. BMC Cancer. 2024;24(1):743.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaghibi A, Jamshidi P, Yazdani J, Rostami F. Identification of Factors Associated with Breast Cancer Screening Based on the PEN-3 Model among Female School Teachers in Kermanshah. Iran J Health Educ Health Promotion. 2016;4(1):58\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcFadden K, Nickel B, Houssami N, Rankin NM, Dodd RH. 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Dev Psychopathol. 2018;30(1):179\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Pelsmacker P, Lewi M, Cauberghe V. The Effect of Personal Characteristics, Perceived Threat, Efficacy and Breast Cancer Anxiety on Breast Cancer Screening Activation. Healthcare. 2017;5(4):65.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cancer, Perception, Risk, Behavioral Research, Psychological Inhibition","lastPublishedDoi":"10.21203/rs.3.rs-8936158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8936158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer remains one of the most common cancers among women worldwide. Behavioral inhibition and risk perception are two psychological factors that may influence screening behaviors. However, few studies have specifically examined the combined impact of behavioral inhibition and risk perception. This study aims to address this gap by elucidating the psychological barriers to breast cancer screening.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis descriptive-analytical, cross-sectional study was conducted in 2024 among women aged 20 and older in the city of Qom. Cluster sampling was employed, resulting in a sample of 315 participants. The standardized questionnaire was used consisted of four sections: demographic information, behavioral inhibition, breast cancer risk perception, and screening behavior. The tools used demonstrated adequate validity and reliability, and the data were analyzed using SPSS software. Statistical tests, including correlation coefficients and logistic regression, were utilized to explore the relationships among the variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of the participants was 34.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41 years. Pearson correlation coefficient analysis indicated a significant relationship between behavior and components of behavioral inhibition (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, r\u0026thinsp;=\u0026thinsp;0.259) and risk perception (p\u0026thinsp;=\u0026thinsp;0.033, r = -0.120). Furthermore, the regression analysis revealed that both behavioral inhibition and risk perception could predict screening behavior (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with risk perception having a greater contribution to the prediction of behavior.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe present study indicates that behavioral inhibition acts as a barrier while risk perception serves as a facilitating factor, which can guide therapeutic and psychological interventions.\u003c/p\u003e","manuscriptTitle":"Predicting breast cancer screening behavior based on childhood and adolescent behavioral inhibition and risk perception in women over 20 years old","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-04 10:29:03","doi":"10.21203/rs.3.rs-8936158/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T10:09:06+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"86561152601209368137331755420549898298","date":"2026-03-05T02:22:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T18:59:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T09:46:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183611601524873250676557865018974424321","date":"2026-03-02T09:29:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266198886937181992052496541202667968039","date":"2026-03-02T02:56:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125830708989923313071074483544599030803","date":"2026-03-01T10:04:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253367103022955128553246709498138427268","date":"2026-02-28T04:53:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T09:12:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T03:43:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T03:41:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-22T00:08:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a12f9b8e-e635-4f82-a72a-f06e24c56e90","owner":[],"postedDate":"March 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T10:26:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-04 10:29:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8936158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8936158","identity":"rs-8936158","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}