Barriers to Care and Internalized Gender Stigma Among Women in Zambia: A DHS-Based Analysis of Psychosocial Vulnerability.

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Newton Nyirenda, Whiteson Mbele, Azielyn Mutiibwa, Jackson Shawa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6947881/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Structural and cultural determinants such as access barriers and gender-based stigma are key psychosocial stressors affecting women’s health in sub-Saharan Africa. While education and wealth are known to influence health access and gender norms, few studies have quantified these associations using nationally representative data. Objectives This study aimed to explore how sociodemographic factors relate to (1) reported challenges in accessing healthcare and (2) the belief that wife-beating is justified, among Zambian women aged 15–49 years, as indicators of underlying structural and cultural vulnerabilities. Methods A cross-sectional analysis was performed on responses from 13,683 women aged 15–49 years participating in the Zambia Demographic and Health Survey. Two outcomes were assessed: (1) reported barriers to accessing healthcare (distance, permission, cost, or not wanting to go alone), and (2) acceptance of wife-beating under specific circumstances, a proxy for internalized gender stigma. Key predictors in the model were women’s educational attainment, household economic status, and residential location (urban versus rural). Weighted logistic regression models were fitted to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Results Barriers to care were more prevalent among rural women (53%) compared to urban women (25%). In adjusted models, women in the richest households had significantly lower odds of reporting care barriers (AOR: 0.22; 95% CI: 0.16–0.30) compared to the poorest. Rural residence increased the odds of barriers (AOR: 1.65; 95% CI: 1.28–2.12), while education was not independently significant. Women with higher educational attainment and greater household wealth were less likely to view wife-beating as acceptable. In comparison to women without formal education, those with higher education levels were 75% less likely to justify wife-beating (AOR: 0.25; 95% CI: 0.18–0.35). Beliefs justifying wife-beating were significantly less common among women in the highest wealth quintile, with an adjusted odds ratio of 0.34 (95% CI: 0.26–0.44). No statistically significant difference remained between urban and rural residence, after controlling for relevant factors. Conclusion Economic status and education strongly predict structural and cultural markers of psychosocial stress among Zambian women. Interventions addressing gender stigma and care access must integrate economic empowerment strategies and target rural populations. These findings support the need for gender-responsive, mental health–informed health policy in Zambia and similar contexts. Maternal & Fetal Medicine Psychiatry Barriers to healthcare Gender norms Internalized stigma Wife-beating justification Psychosocial stress Zambia DHS Women’s health Low- and middle-income countries (LMICs) Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Access to health care and the normalization of gender-based violence remain pervasive challenges for women in sub-Saharan Africa, contributing to profound disparities in health outcomes and well-being. In Zambia, although maternal and reproductive health indicators have improved over the past two decades, structural and cultural barriers continue to impede equitable health access and reinforce harmful gender norms 1 . Disparities in health service utilization are not merely logistical but are often rooted in psychosocial and sociocultural determinants such as autonomy, stigma, and internalized gender norms 2 – 5 . Psychological stress is increasingly recognized as a product of broader social and environmental factors, including limited agency, poverty, and exposure to gendered violence 6 – 10 . Within this framework, proxies such as barriers to healthcare access and attitudes that justify wife-beating offer insight into the underlying social ecology of women’s mental health. Studies from similar settings have shown that women who face difficulty obtaining permission to seek care, experience transportation or cost barriers, or live in male-dominated decision-making environments are at increased risk for mental health challenges and adverse health conditions. 11 – 13 . Cultural acceptance of wife-beating, a potent marker of internalized gender stigma, is also widespread in Zambia, with DHS 2018 data indicating that nearly half of women agree with at least one justification for wife-beating under specific circumstances 1 . Such attitudes, often shaped by early life experiences and community norms, are associated not only with intimate partner violence (IPV) victimization but also with lower health-seeking behavior and poorer mental health outcomes 14 , 15 . The normalization of gender violence through these attitudes reflects deep-rooted inequities that extend beyond individual beliefs and highlight the need for structural interventions 16 . Importantly, social determinants such as education, household wealth, and rural residence intersect with these psychosocial stressors to further shape health access and gender norms. Higher education and household wealth have been consistently associated with increased autonomy and lower tolerance for violence across multiple LMIC settings 17 – 19 . In Zambia, these gradients remain evident, yet few studies have jointly examined their relationship with both structural and cultural proxies of psychological vulnerability in a nationally representative context. Given the scarcity of population-level mental health indicators in low- and middle-income countries (LMICs), these proxies offer valuable insight into the distribution and determinants of psychosocial burden, thereby informing equity-centered health policies where direct data are lacking. This study aims to fill this gap by using data from the 2018 Zambia Demographic and Health Survey (ZDHS) to explore how sociodemographic factors are associated with (1) reported difficulties in accessing healthcare and (2) the belief that wife-beating is acceptable among reproductive-aged women in Zambia. By highlighting these intersecting axes of disadvantage, we seek to inform the design of integrated gender-responsive and mental health–sensitive health policies in Zambia and similar contexts. METHODS Study Design This was a cross-sectional analysis utilizing data from the 2018 Zambia Demographic and Health Survey (ZDHS), a nationally representative household survey conducted by Zambia’s Central Statistical Office in collaboration with the Ministry of Health and ICF International. Sampling for the ZDHS followed a stratified, two-stage cluster design, with geographic enumeration areas selected in the first stage and households sampled within each cluster. Study Population The study involved women aged 15 to 49 who participated in the individual woman’s questionnaire. These participants provided valuable data on various sociodemographic characteristics and psychosocial factors, which were crucial for our analysis. We excluded individuals lacking data on education, residence, wealth or either of the primary outcome variables from the analysis. The final analytic sample included 13,683 women. Measures Outcomes We examined two primary psychosocial outcomes; (1) barriers to healthcare access, defined as reporting at least one major access difficulty (distance, permission, cost, or going alone), based on DHS variables v467a–v467d; and (2) internalized stigma acceptance, defined as responding affirmatively ("yes") to at least one justification for wife-beating under various circumstances (v744a–v744e). Wife-beating justification has been widely used in global health research as a validated proxy for internalized gender stigma, reflecting socialization into harmful norms that perpetuate gender-based violence and suppress agency. Prior studies have linked these attitudes to poor mental health outcomes, reduced autonomy, and increased risk of intimate partner violence 20 , 21 . Predictors Key independent variables included education level ( v106 : no education, primary, secondary, higher), household wealth quintile ( v190 : poorest to richest), and urban vs. rural residence ( v025 ). These were selected based on theoretical relevance to both structural and cultural determinants of gender-based stress. Statistical Analysis We conducted weighted descriptive analyses and multivariable logistic regression using the R survey package to account for the complex sampling design of the DHS. Sampling weights ( v005 ) were scaled (divided by 1,000,000) and used along with primary sampling unit ( v021 ) and stratification ( v022 ) variables to construct the survey design object. All models were fit using svyglm with a quasibinomial family to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Statistical significance was set at p < 0.05. Ethical Considerations This analysis is based on publicly available, de-identified secondary data from the DHS Program. The 2018 ZDHS received prior ethics approval from both the Tropical Diseases Research Centre in Zambia and ICF's Institutional Review Board. During the original survey, all participants provided written informed consent. Since this study involved secondary analysis of anonymized, publicly available data, additional ethical approval was not required. RESULTS Table 1 Characteristics of Women Aged 15–49 and Distribution of Structural and Gender-Based Vulnerabilities, Zambia DHS 2018 (N = 13,683) Characteristic n % Reporting Barriers to Care % Accepting Wife-Beating Education Level No education 1,145 52.6% 51.8% Primary 6,217 47.7% 53.5% Secondary 5,556 31.5% 39.5% Higher 765 23.1% 11.0% Wealth Index Poorest 2,844 60.5% 57.4% Poorer 2,677 52.6% 57.3% Middle 2,683 47.4% 50.3% Richer 2,559 28.9% 41.4% Richest 2,920 20.0% 26.9% Residence Urban 5,513 24.8% 36.5% Rural 8,170 52.9% 52.5% Most women surveyed had received formal education, with primary and secondary schooling being the most common levels completed (45.4% and 40.6%, respectively).Nearly 60% lived in rural areas. Healthcare access challenges were especially prominent among women with no formal schooling and those in the lowest wealth category, with 52.6% and 60.5% reporting barriers, respectively. Similarly, justification of wife-beating was most prevalent among women with no education (51.8%) and those from the poorest households (57.4%). In contrast, women with higher education reported markedly lower levels of both stressors, with only 23.1% citing barriers to care and 11.0% endorsing wife-beating. Notable disparities existed between rural and urban respondents. Over half of rural women reported healthcare access difficulties (52.9%), and a similar proportion accepted wife-beating as justifiable (52.5%). In contrast, urban women's rates were substantially lower (24.8% and 36.5%, respectively), indicating a pronounced geographic gap in psychosocial stress exposure. Table 2 Adjusted Odds Ratios for Experiencing Barriers to Healthcare Access by Sociodemographic Factors Among Women in Zambia, 2018 DHS (DHS 2018) Predictor Adjusted Odds Ratio (AOR) 95% CI p-value Education Level Primary vs. None 0.98 [0.83, 1.15] 0.783 Secondary vs. None 0.85 [0.69, 1.05] 0.102 Higher vs. None 0.94 [0.72, 1.22] 0.632 Wealth Index Poorer vs. Poorest 0.75 [0.64, 0.89] < 0.001 *** Middle vs. Poorest 0.70 [0.58, 0.84] < 0.001 *** Richer vs. Poorest 0.42 [0.34, 0.52] < 0.001 *** Richest vs. Poorest 0.28 [0.22, 0.36] < 0.001 *** Residence Rural vs. Urban 1.65 [1.28, 2.13] < 0.001 *** * Reference groups: No education, Poorest wealth quintile, Urban residence Barriers to healthcare access were strongly patterned along socioeconomic and geographic lines. Women from wealthier households were increasingly less likely to report difficulties accessing healthcare, demonstrating a gradient effect with rising economic status. In particular, those in the richest households were 78% less likely to report barriers compared to those in the poorest quintile (AOR: 0.22; 95% CI: 0.16–0.30), highlighting the protective effect of economic resources. Rural residence emerged as another significant determinant, with women living in rural areas exhibiting 65% higher odds of experiencing barriers to care compared to their urban counterparts (AOR: 1.65; 95% CI: 1.28–2.12). Interestingly, educational attainment did not show a statistically significant association with reported barriers in the adjusted model, suggesting that while education may enhance awareness and health-seeking behaviors, it may not be sufficient to overcome structural constraints such as distance, cost, or facility availability. Table 3 Adjusted Odds Ratios for Acceptance of Gender-Based Violence by Sociodemographic Factors Among Women in Zambia, 2018 DHS (DHS 2018) Variable AOR 95% CI p-value Intercept 1.38 [1.03, 1.85] 0.031 Primary education (vs. none) 1.18 [1.00, 1.40] 0.046 Secondary education 0.92 [0.77, 1.10] 0.366 Higher education 0.25 [0.18, 0.36] < 0.001 Poorer (vs. poorest) 1.01 [0.87, 1.18] 0.893 Middle 0.76 [0.64, 0.91] 0.001 Richer 0.53 [0.43, 0.66] < 0.001 Richest 0.34 [0.27, 0.45] < 0.001 Rural (vs. urban) 0.88 [0.71, 1.09] 0.236 * Reference groups: No education, Poorest wealth quintile, Urban residence Socioeconomic indicators such as education and household wealth strongly influenced women's attitudes toward wife-beating. Women with higher educational attainment were significantly less likely to justify wife-beating, with those holding advanced education showing a 75% lower likelihood compared to women without formal education (AOR: 0.25; 95% CI: 0.18–0.35). A similar protective pattern was observed across wealth quintiles, as women in the richest group had 66% lower odds of endorsing wife-beating (AOR: 0.34; 95% CI: 0.26–0.44) compared to the poorest group. Notably, urban versus rural residence was not independently associated with stigma acceptance in the adjusted model. DISCUSSION This study reveals how structural and cultural stressors, proxied by barriers to care and acceptance of wife-beating, intersect with education, wealth, and geography to shape women's psychosocial vulnerability in Zambia. While previous studies have often examined these constructs in isolation, our combined analysis offers novel insight into how both structural access constraints and internalized stigma may co-occur and reinforce each other within marginalized groups. This dual approach contributes to a more holistic understanding of how social determinants affect mental health risk environments in low-resource settings. Our findings demonstrate that wealth and rural residence are strongly associated with structural barriers to healthcare, consistent with prior research across sub-Saharan Africa (SSA) that identifies cost, distance, and poor infrastructure as persistent impediments to service utilization, even in countries approaching universal health coverage goals 22 – 24 . Education, in contrast, showed no significant association with barriers in the adjusted model. This suggests that while education may enhance knowledge or intention to seek care, its effects may be insufficient in the face of environmental and systemic constraints such as transport, cost, or decision-making norms 25 , 26 . In contrast, education had a robust protective effect on acceptance of wife-beating, a proxy for internalized gender stigma, with women who attained higher education exhibiting 75% lower odds of justification. This finding echoes prior SSA research suggesting that formal schooling can empower women to reject harmful social norms and assert greater agency 21 , 26 . Interestingly, household wealth also conferred protective effects against stigma, particularly in the higher quintiles, reflecting the potential of economic security to buffer against normative pressures that perpetuate gender violence 20 , 21 , 26 . The observed lack of association between rural residence and acceptance of wife-beating is somewhat unexpected. Given that rural communities often display stronger adherence to patriarchal norms, one might anticipate a stronger rural effect. This null finding could reflect measurement limitations, such as unmeasured community-level variation, or possibly the homogenization of cultural attitudes across urban–rural lines through mass media and public health campaigns. Future qualitative or multilevel analyses may be needed to disentangle these contextual nuances. Our analysis also has implications beyond structural policy. The strong association between social disadvantage and these psychosocial proxies suggests that barriers to care and stigma could serve as early warning signals for mental health risk. Integrating questions on healthcare access difficulty and gender norm acceptance into routine community health assessments could help flag high-risk women who may benefit from targeted mental health screening or psychosocial support. In the absence of robust mental health surveillance infrastructure, such proxies offer a feasible and contextually grounded approach for early identification in LMICs. This study has several strengths. First, it uses a large, nationally representative sample with validated DHS measures, enhancing the external validity of our findings for Zambia and similar contexts. Second, it applies rigorous survey-weighted analyses to account for the complex sampling design, lending robustness to our estimates. Third, the joint modelling of both structural and cultural stressors allows for a more comprehensive mapping of psychosocial vulnerability, an approach not commonly employed in existing literature. Nonetheless, limitations exist. First, although these indicators provide useful information on the social and cultural factors influencing mental well-being, they do not directly assess psychological symptoms and are not a replacement for formal clinical tools used to evaluate mental health. Second, given the cross-sectional nature of the data, we are limited to identifying associations rather than determining causality. Finally, potential residual confounding from factors like local healthcare system efficiency or community-level stigma may still influence the results. Taken together, our findings point to the need for integrated, multi-level interventions that address both the tangible and intangible dimensions of women’s stress environments. Expanding access to transportation and reducing user fees may alleviate structural barriers, while educational investments and anti-stigma campaigns may help shift harmful norms. Importantly, these strategies should be co-designed with community input to ensure cultural relevance and sustainability. In doing so, health systems can move closer to gender-responsive and mentally inclusive care models that acknowledge, not ignore, the psychosocial burdens many women face. CONCLUSION Our study demonstrates the dual burden of structural and gender-based stressors among Zambian women and shows how wealth and education independently shape access to care and tolerance of violence. While financial disadvantage and rural residence elevate structural barriers, higher education emerges as a key protective factor against internalized gender stigma. These findings support the need for integrated strategies that simultaneously address the physical accessibility of health services and the social norms that perpetuate psychological harm. Policy efforts should combine infrastructure investments, such as transport and cost reduction, with gender-transformative programs that expand educational opportunities and challenge harmful beliefs. As Zambia and other LMICs seek to scale mental health responses, leveraging such DHS-based proxies may offer a practical tool for identifying high-risk populations and designing more equitable, responsive systems of care. Abbreviations AOR Adjusted Odds Ratio CI Confidence Interval DHS Demographic and Health Survey IPV Intimate Partner Violence LMIC Low–and Middle–Income Country SSA Sub–Saharan Africa ZDHS Zambia Demographic and Health Survey Declarations Ethics approval and consent to participate This study is a secondary analysis of publicly available, de-identified data from the 2018 Zambia Demographic and Health Survey (ZDHS). The original survey received ethical approval from the Tropical Diseases Research Centre (TDRC) in Zambia and the Institutional Review Board of ICF International. Written informed consent was obtained from all participants at the time of data collection. No further ethical approval was required for this secondary analysis. Consent for publication Not applicable. Competing interests The author declares that there are no competing interests. Funding No specific funding was received for this study. Authors' contributions NN and WM conceptualized the study, conducted the data analysis, and wrote the original draft. AM and JS contributed to review and editing of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors acknowledge the Demographic and Health Surveys (DHS) Program for granting access to the 2018 Zambia DHS data. Availability of data and materials The datasets analyzed during the current study are available from the DHS Program repository: https://dhsprogram.com/data/available-datasets.cfm References Statistics Agency Z (2020) GOVERNMENT OF ZAMBIA Zambia Demographic and Health Survey 2018 .; Latif AS (2020) The Importance of Understanding Social and Cultural Norms in Delivering Quality Health Care—A Personal Experience Commentary. 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2018.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6947881/v1/6431a8d4ce415ea9d3ead2c5.png"},{"id":85299448,"identity":"47911569-e005-4ae0-a375-250eb91cbe3f","added_by":"auto","created_at":"2025-06-24 11:48:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural and cultural stress by household wealth among women aged 15-49, Zambia DHS 2018.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6947881/v1/f7338de45f23cdd0c5e52a98.png"},{"id":85299090,"identity":"97455700-5ecc-4aa2-ae78-037970ee6d8a","added_by":"auto","created_at":"2025-06-24 11:40:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted odds ratios for barriers to healthcare access among women aged 15-49, Zambia DHS 2018.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6947881/v1/976977d835b128f5262413d0.png"},{"id":85299447,"identity":"70babf84-cb69-4fdc-a0f7-b6ab3a954272","added_by":"auto","created_at":"2025-06-24 11:48:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted odds ratios for stigma acceptance among women aged 15-49, Zambia DHS 2018.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6947881/v1/dc3720f47b88fa2f95486779.png"},{"id":85301203,"identity":"17917ad2-e6b7-4973-aa94-f79698bdc4bc","added_by":"auto","created_at":"2025-06-24 12:04:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6947881/v1/137e1fba-9e76-4aac-8d4f-0ff8ba59d57e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBarriers to Care and Internalized Gender Stigma Among Women in Zambia: A DHS-Based Analysis of Psychosocial Vulnerability.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAccess to health care and the normalization of gender-based violence remain pervasive challenges for women in sub-Saharan Africa, contributing to profound disparities in health outcomes and well-being. In Zambia, although maternal and reproductive health indicators have improved over the past two decades, structural and cultural barriers continue to impede equitable health access and reinforce harmful gender norms \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Disparities in health service utilization are not merely logistical but are often rooted in psychosocial and sociocultural determinants such as autonomy, stigma, and internalized gender norms \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePsychological stress is increasingly recognized as a product of broader social and environmental factors, including limited agency, poverty, and exposure to gendered violence \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Within this framework, proxies such as barriers to healthcare access and attitudes that justify wife-beating offer insight into the underlying social ecology of women\u0026rsquo;s mental health. Studies from similar settings have shown that women who face difficulty obtaining permission to seek care, experience transportation or cost barriers, or live in male-dominated decision-making environments are at increased risk for mental health challenges and adverse health conditions. \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCultural acceptance of wife-beating, a potent marker of internalized gender stigma, is also widespread in Zambia, with DHS 2018 data indicating that nearly half of women agree with at least one justification for wife-beating under specific circumstances \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Such attitudes, often shaped by early life experiences and community norms, are associated not only with intimate partner violence (IPV) victimization but also with lower health-seeking behavior and poorer mental health outcomes \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The normalization of gender violence through these attitudes reflects deep-rooted inequities that extend beyond individual beliefs and highlight the need for structural interventions \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImportantly, social determinants such as education, household wealth, and rural residence intersect with these psychosocial stressors to further shape health access and gender norms. Higher education and household wealth have been consistently associated with increased autonomy and lower tolerance for violence across multiple LMIC settings \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In Zambia, these gradients remain evident, yet few studies have jointly examined their relationship with both structural and cultural proxies of psychological vulnerability in a nationally representative context. Given the scarcity of population-level mental health indicators in low- and middle-income countries (LMICs), these proxies offer valuable insight into the distribution and determinants of psychosocial burden, thereby informing equity-centered health policies where direct data are lacking.\u003c/p\u003e \u003cp\u003eThis study aims to fill this gap by using data from the 2018 Zambia Demographic and Health Survey (ZDHS) to explore how sociodemographic factors are associated with (1) reported difficulties in accessing healthcare and (2) the belief that wife-beating is acceptable among reproductive-aged women in Zambia. By highlighting these intersecting axes of disadvantage, we seek to inform the design of integrated gender-responsive and mental health\u0026ndash;sensitive health policies in Zambia and similar contexts.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional analysis utilizing data from the 2018 Zambia Demographic and Health Survey (ZDHS), a nationally representative household survey conducted by Zambia\u0026rsquo;s Central Statistical Office in collaboration with the Ministry of Health and ICF International. Sampling for the ZDHS followed a stratified, two-stage cluster design, with geographic enumeration areas selected in the first stage and households sampled within each cluster.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study involved women aged 15 to 49 who participated in the individual woman\u0026rsquo;s questionnaire. These participants provided valuable data on various sociodemographic characteristics and psychosocial factors, which were crucial for our analysis. We excluded individuals lacking data on education, residence, wealth or either of the primary outcome variables from the analysis. The final analytic sample included 13,683 women.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eWe examined two primary psychosocial outcomes; (1) barriers to healthcare access, defined as reporting at least one major access difficulty (distance, permission, cost, or going alone), based on DHS variables v467a\u0026ndash;v467d; and (2) internalized stigma acceptance, defined as responding affirmatively (\"yes\") to at least one justification for wife-beating under various circumstances (v744a\u0026ndash;v744e). Wife-beating justification has been widely used in global health research as a validated proxy for internalized gender stigma, reflecting socialization into harmful norms that perpetuate gender-based violence and suppress agency. Prior studies have linked these attitudes to poor mental health outcomes, reduced autonomy, and increased risk of intimate partner violence \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictors\u003c/h3\u003e\n\u003cp\u003eKey independent variables included education level (\u003cem\u003ev106\u003c/em\u003e: no education, primary, secondary, higher), household wealth quintile (\u003cem\u003ev190\u003c/em\u003e: poorest to richest), and urban vs. rural residence (\u003cem\u003ev025\u003c/em\u003e). These were selected based on theoretical relevance to both structural and cultural determinants of gender-based stress.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe conducted weighted descriptive analyses and multivariable logistic regression using the R survey package to account for the complex sampling design of the DHS. Sampling weights (\u003cem\u003ev005\u003c/em\u003e) were scaled (divided by 1,000,000) and used along with primary sampling unit (\u003cem\u003ev021\u003c/em\u003e) and stratification (\u003cem\u003ev022\u003c/em\u003e) variables to construct the survey design object. All models were fit using svyglm with a quasibinomial family to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis analysis is based on publicly available, de-identified secondary data from the DHS Program. The 2018 ZDHS received prior ethics approval from both the Tropical Diseases Research Centre in Zambia and ICF's Institutional Review Board. During the original survey, all participants provided written informed consent. Since this study involved secondary analysis of anonymized, publicly available data, additional ethical approval was not required.\u003c/p\u003e"},{"header":"RESULTS","content":"\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\u003eCharacteristics of Women Aged 15\u0026ndash;49 and Distribution of Structural and Gender-Based Vulnerabilities, Zambia DHS 2018 (N\u0026thinsp;=\u0026thinsp;13,683)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Reporting Barriers to Care\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Accepting Wife-Beating\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.8%\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.5%\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.5%\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\u003eMost women surveyed had received formal education, with primary and secondary schooling being the most common levels completed (45.4% and 40.6%, respectively).Nearly 60% lived in rural areas. Healthcare access challenges were especially prominent among women with no formal schooling and those in the lowest wealth category, with 52.6% and 60.5% reporting barriers, respectively. Similarly, justification of wife-beating was most prevalent among women with no education (51.8%) and those from the poorest households (57.4%). In contrast, women with higher education reported markedly lower levels of both stressors, with only 23.1% citing barriers to care and 11.0% endorsing wife-beating. Notable disparities existed between rural and urban respondents. Over half of rural women reported healthcare access difficulties (52.9%), and a similar proportion accepted wife-beating as justifiable (52.5%). In contrast, urban women's rates were substantially lower (24.8% and 36.5%, respectively), indicating a pronounced geographic gap in psychosocial stress exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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\u003eAdjusted Odds Ratios for Experiencing Barriers to Healthcare Access by Sociodemographic Factors Among Women in Zambia, 2018 DHS (DHS 2018)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted Odds Ratio (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary vs. None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.83, 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary vs. None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.69, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher vs. None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.72, 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer vs. Poorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.64, 0.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle vs. Poorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.58, 0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher vs. Poorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.34, 0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest vs. Poorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.22, 0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural vs. Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.28, 2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Reference groups: No education, Poorest wealth quintile, Urban residence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBarriers to healthcare access were strongly patterned along socioeconomic and geographic lines. Women from wealthier households were increasingly less likely to report difficulties accessing healthcare, demonstrating a gradient effect with rising economic status. In particular, those in the richest households were 78% less likely to report barriers compared to those in the poorest quintile (AOR: 0.22; 95% CI: 0.16\u0026ndash;0.30), highlighting the protective effect of economic resources. Rural residence emerged as another significant determinant, with women living in rural areas exhibiting 65% higher odds of experiencing barriers to care compared to their urban counterparts (AOR: 1.65; 95% CI: 1.28\u0026ndash;2.12). Interestingly, educational attainment did not show a statistically significant association with reported barriers in the adjusted model, suggesting that while education may enhance awareness and health-seeking behaviors, it may not be sufficient to overcome structural constraints such as distance, cost, or facility availability.\u003c/p\u003e \u003cp\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\u003eAdjusted Odds Ratios for Acceptance of Gender-Based Violence by Sociodemographic Factors Among Women in Zambia, 2018 DHS (DHS 2018)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \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\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.03, 1.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education (vs. none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.00, 1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.77, 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.18, 0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer (vs. poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.87, 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.64, 0.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.43, 0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.27, 0.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural (vs. urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.71, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Reference groups: No education, Poorest wealth quintile, Urban residence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSocioeconomic indicators such as education and household wealth strongly influenced women's attitudes toward wife-beating. Women with higher educational attainment were significantly less likely to justify wife-beating, with those holding advanced education showing a 75% lower likelihood compared to women without formal education (AOR: 0.25; 95% CI: 0.18\u0026ndash;0.35). A similar protective pattern was observed across wealth quintiles, as women in the richest group had 66% lower odds of endorsing wife-beating (AOR: 0.34; 95% CI: 0.26\u0026ndash;0.44) compared to the poorest group. Notably, urban versus rural residence was not independently associated with stigma acceptance in the adjusted model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study reveals how structural and cultural stressors, proxied by barriers to care and acceptance of wife-beating, intersect with education, wealth, and geography to shape women's psychosocial vulnerability in Zambia. While previous studies have often examined these constructs in isolation, our combined analysis offers novel insight into how both structural access constraints and internalized stigma may co-occur and reinforce each other within marginalized groups. This dual approach contributes to a more holistic understanding of how social determinants affect mental health risk environments in low-resource settings.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that wealth and rural residence are strongly associated with structural barriers to healthcare, consistent with prior research across sub-Saharan Africa (SSA) that identifies cost, distance, and poor infrastructure as persistent impediments to service utilization, even in countries approaching universal health coverage goals \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Education, in contrast, showed no significant association with barriers in the adjusted model. This suggests that while education may enhance knowledge or intention to seek care, its effects may be insufficient in the face of environmental and systemic constraints such as transport, cost, or decision-making norms \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, education had a robust protective effect on acceptance of wife-beating, a proxy for internalized gender stigma, with women who attained higher education exhibiting 75% lower odds of justification. This finding echoes prior SSA research suggesting that formal schooling can empower women to reject harmful social norms and assert greater agency \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Interestingly, household wealth also conferred protective effects against stigma, particularly in the higher quintiles, reflecting the potential of economic security to buffer against normative pressures that perpetuate gender violence \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe observed lack of association between rural residence and acceptance of wife-beating is somewhat unexpected. Given that rural communities often display stronger adherence to patriarchal norms, one might anticipate a stronger rural effect. This null finding could reflect measurement limitations, such as unmeasured community-level variation, or possibly the homogenization of cultural attitudes across urban\u0026ndash;rural lines through mass media and public health campaigns. Future qualitative or multilevel analyses may be needed to disentangle these contextual nuances.\u003c/p\u003e \u003cp\u003eOur analysis also has implications beyond structural policy. The strong association between social disadvantage and these psychosocial proxies suggests that barriers to care and stigma could serve as early warning signals for mental health risk. Integrating questions on healthcare access difficulty and gender norm acceptance into routine community health assessments could help flag high-risk women who may benefit from targeted mental health screening or psychosocial support. In the absence of robust mental health surveillance infrastructure, such proxies offer a feasible and contextually grounded approach for early identification in LMICs.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, it uses a large, nationally representative sample with validated DHS measures, enhancing the external validity of our findings for Zambia and similar contexts. Second, it applies rigorous survey-weighted analyses to account for the complex sampling design, lending robustness to our estimates. Third, the joint modelling of both structural and cultural stressors allows for a more comprehensive mapping of psychosocial vulnerability, an approach not commonly employed in existing literature.\u003c/p\u003e \u003cp\u003eNonetheless, limitations exist. First, although these indicators provide useful information on the social and cultural factors influencing mental well-being, they do not directly assess psychological symptoms and are not a replacement for formal clinical tools used to evaluate mental health. Second, given the cross-sectional nature of the data, we are limited to identifying associations rather than determining causality. Finally, potential residual confounding from factors like local healthcare system efficiency or community-level stigma may still influence the results.\u003c/p\u003e \u003cp\u003eTaken together, our findings point to the need for integrated, multi-level interventions that address both the tangible and intangible dimensions of women\u0026rsquo;s stress environments. Expanding access to transportation and reducing user fees may alleviate structural barriers, while educational investments and anti-stigma campaigns may help shift harmful norms. Importantly, these strategies should be co-designed with community input to ensure cultural relevance and sustainability. In doing so, health systems can move closer to gender-responsive and mentally inclusive care models that acknowledge, not ignore, the psychosocial burdens many women face.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study demonstrates the dual burden of structural and gender-based stressors among Zambian women and shows how wealth and education independently shape access to care and tolerance of violence. While financial disadvantage and rural residence elevate structural barriers, higher education emerges as a key protective factor against internalized gender stigma. These findings support the need for integrated strategies that simultaneously address the physical accessibility of health services and the social norms that perpetuate psychological harm. Policy efforts should combine infrastructure investments, such as transport and cost reduction, with gender-transformative programs that expand educational opportunities and challenge harmful beliefs. As Zambia and other LMICs seek to scale mental health responses, leveraging such DHS-based proxies may offer a practical tool for identifying high-risk populations and designing more equitable, responsive systems of care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIPV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntimate Partner Violence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLMIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow\u0026ndash;and Middle\u0026ndash;Income Country\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSSA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSub\u0026ndash;Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eZDHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZambia Demographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of publicly available, de-identified data from the 2018 Zambia Demographic and Health Survey (ZDHS). The original survey received ethical approval from the Tropical Diseases Research Centre (TDRC) in Zambia and the Institutional Review Board of ICF International. Written informed consent was obtained from all participants at the time of data collection. No further ethical approval was required for this secondary analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eNN and WM conceptualized the study, conducted the data analysis, and wrote the original draft. AM and JS contributed to review and editing of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors acknowledge the Demographic and Health Surveys (DHS) Program for granting access to the 2018 Zambia DHS data.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the DHS Program repository:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/data/available-datasets.cfm\u003c/span\u003e\u003c/span\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStatistics Agency Z (2020) \u003cem\u003eGOVERNMENT OF ZAMBIA Zambia Demographic and Health Survey 2018\u003c/em\u003e.; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.DHSprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatif AS (2020) The Importance of Understanding Social and Cultural Norms in Delivering Quality Health Care\u0026mdash;A Personal Experience Commentary. 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Reprod Health 12(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12978-015-0078-8\u003c/span\u003e\u003cspan address=\"10.1186/s12978-015-0078-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGwilliam K, Foster V, Archondo-Callao R, Brice\u0026ntilde;o-Garmendia C, Nogales A, Sethi K (2008) \u003cem\u003eAFRICA INFRASTRUCTURE COUNTRY DIAGNOSTIC Roads in Sub-Saharan Africa\u003c/em\u003e.; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.infrastructureafrica.org\u003c/span\u003e\u003cspan address=\"http://www.infrastructureafrica.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerdous Khan T, Qian L \u003cem\u003eDeterminants of Women\u0026rsquo;s Attitude towards Intimate Partner Violence: Evidence from Bangladesh\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEr-Rays Y, Meriem D Evaluating the Financial Factors Influencing Maternal, Newborn, and Child Health in Africa. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2024.02.22.24303217v1\u003c/span\u003e\u003cspan address=\"10.1101/2024.02.22.24303217v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Barriers to healthcare, Gender norms, Internalized stigma, Wife-beating justification, Psychosocial stress, Zambia, DHS, Women’s health, Low- and middle-income countries (LMICs)","lastPublishedDoi":"10.21203/rs.3.rs-6947881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6947881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\n\u0026nbsp;Structural and cultural determinants such as access barriers and gender-based stigma are key psychosocial stressors affecting women’s health in sub-Saharan Africa. While education and wealth are known to influence health access and gender norms, few studies have quantified these associations using nationally representative data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003cbr\u003e\n\u0026nbsp;This study aimed to explore how sociodemographic factors relate to (1) reported challenges in accessing healthcare and (2) the belief that wife-beating is justified, among Zambian women aged 15–49 years, as indicators of underlying structural and cultural vulnerabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\n\u0026nbsp;A cross-sectional analysis was performed on responses from 13,683 women aged 15–49 years participating in the Zambia Demographic and Health Survey. Two outcomes were assessed: (1) reported barriers to accessing healthcare (distance, permission, cost, or not wanting to go alone), and (2) acceptance of wife-beating under specific circumstances, a proxy for internalized gender stigma. Key predictors in the model were women’s educational attainment, household economic status, and residential location (urban versus rural). Weighted logistic regression models were fitted to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\n\u0026nbsp;Barriers to care were more prevalent among rural women (53%) compared to urban women (25%). In adjusted models, women in the richest households had significantly lower odds of reporting care barriers (AOR: 0.22; 95% CI: 0.16–0.30) compared to the poorest. Rural residence increased the odds of barriers (AOR: 1.65; 95% CI: 1.28–2.12), while education was not independently significant.\u003c/p\u003e\n\u003cp\u003eWomen with higher educational attainment and greater household wealth were less likely to view wife-beating as acceptable. In comparison to women without formal education, those with higher education levels were 75% less likely to justify wife-beating (AOR: 0.25; 95% CI: 0.18–0.35). Beliefs justifying wife-beating were significantly less common among women in the highest wealth quintile, with an adjusted odds ratio of 0.34 (95% CI: 0.26–0.44). No statistically significant difference remained between urban and rural residence, after controlling for relevant factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nEconomic status and education strongly predict structural and cultural markers of psychosocial stress among Zambian women. Interventions addressing gender stigma and care access must integrate economic empowerment strategies and target rural populations. These findings support the need for gender-responsive, mental health–informed health policy in Zambia and similar contexts.\u003c/p\u003e","manuscriptTitle":"Barriers to Care and Internalized Gender Stigma Among Women in Zambia: A DHS-Based Analysis of Psychosocial Vulnerability.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 11:40:28","doi":"10.21203/rs.3.rs-6947881/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db8f9105-50eb-460c-97e0-16c413f6bdc4","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50398303,"name":"Maternal \u0026 Fetal Medicine"},{"id":50398304,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-06-24T11:40:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 11:40:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6947881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6947881","identity":"rs-6947881","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}