Social determinants in health and mental well-being among out-patients

International Journal of Public Health Science (IJPHS) Vol. 14, No. 4, December 2025, pp. 1886~1896 ISSN: 2252-8806, DOI: 10.11591/ijphs.v14i4.26588  1886 Journal homepage: http://ijphs.iaescore.com Social determinants in health and mental well-being among out-patients Rainier C. Moreno-Lacalle, Pilar Bianca Mae G. Ducusin, Rayvine B. Suma-il, Lana Stephanie A. Tiu, Kyla Dee Manantan, Karyle Myara E. Borromeo, Amanda Nicole N. Cruz, Romeo Benedict Landagora, Chad Allen M. Nerona, Christan James R. Edualino Saint Louis University, Baguio City, Philippines Article Info ABSTRACT Article history: Received Feb 28, 2025 Revised Aug 26, 2025 Accepted Nov 3, 2025 Social determinants are crucial in shaping mental well-being, yet their specific impact within diverse cultural contexts like the Philippines remains underexplored. This study investigated the influence of social determinants on mental well-being among out-patients with mental health conditions. A retrospective cross-sectional design was used, analyzing existing health records (n = 21,813) from 2019 to 2024 and survey questionnaires (n = 89) from three psychiatric institutions. Social determinants were classified as proximal or distal, and mental well-being was assessed using the WarwickEdinburgh Mental Well-being Scale (WEMWBS). Results showed that participants generally reported a high level of mental well-being. Regression analysis revealed that while proximal social factors collectively explained the largest variance in well-being (28.6%), they were not statistically significant predictors as a group. Conversely, specific distal factors—notably birth order (p<0.010), parental marital status (p<0.017), and a history of family sexual abuse (p<0.033)—were significant individual predictors. This research provides novel evidence on the application of the proximal-distal framework in a Filipino context, uniquely demonstrating that while broad social categories are influential, specific familial and life-course events are more direct predictors of mental well-being. The findings underscore the need for culturally-sensitive, targeted interventions that address both broad environmental factors and specific individual circumstances to promote health equity. Keywords: Health equity Mental well-being Out-patients Philippines Proximal-distal factors Public health Social determinants This is an open access article under the CC BY-SA license. Corresponding Author: Rainier C. Moreno-Lacalle Saint Louis University Baguio City, Philippines Email: mo[email protected].ph 1. INTRODUCTION The principle that health encompasses complete physical, mental, and social well-being is a foundational concept in global public health [1]. Within this holistic view, mental well-being—an individual's ability to cope with life's stresses, realize their potential, and contribute to their community—is profoundly influenced by the conditions in which they are born, grow, live, work, and age [2], [3]. These conditions, known as the social determinants of health (SDH), include a wide array of social, economic, cultural, and environmental factors that can lead to significant health inequities [4], [5]. A substantial body of international research has established strong links between SDH and mental health outcomes. Factors such as poverty, education level, and employment status are consistently identified as major predictors of mental health conditions [6], [7]. These determinants are often categorized by their Int J Public Health Sci ISSN: 2252-8806  Social determinants in health and mental well-being among out-patients (Rainier C. Moreno-Lacalle) 1887 proximity to the individual: proximal factors, such as household income, have a direct impact, whereas distal factors, like economic inequality and cultural norms, exert an indirect influence [8]. For instance, studies in Great Britain show that households in the lowest income quintile are two to three times more likely to experience mental health issues [9], while research across Sub-Saharan Africa highlights how lower socioeconomic status creates significant barriers to healthcare access [10], [11]. Despite this global evidence, a significant research gap persists in understanding how these social determinants operate within specific, non-Western cultural contexts. While broad factors like income and education are universally relevant, more nuanced social factors tied to cultural and familial structures remain underexplored, particularly in Southeast Asian nations like the Philippines. For example, the country faces unique challenges related to mental healthcare, where strong cultural stigma and inconsistent help-seeking behaviors are major barriers to accessing professional support [12]. This situation suggests that deeper, culturally-specific social dynamics, such as the role of the family, community obligations, and specific lifecourse events, may play a more decisive role in shaping mental well-being than is currently understood. Factors such as birth order, parental marital stability, and histories of intra-familial abuse have been largely overlooked in local research, yet they represent critical aspects of the social environment that could directly impact mental health trajectories. This study was motivated by the need to address this gap by providing a more comprehensive, culturally-situated understanding of how social determinants affect mental well-being among Filipinos with mental health conditions. By applying the proximal-distal framework developed by [13], [14], this research moves beyond universal determinants to investigate the predictive power of these specific, and often unexamined, social factors. This research investigates how social determinants of mental health predict the well-being of patients with mental health conditions, with the specific objectives of identifying common social determinants, determining the level of mental well-being, examining the relationship between social determinants and well-being, and assessing the predictive level of proximal and distal social determinants on patients' well-being. 2. METHOD A retrospective cross-sectional research design was employed to analyze the role of social determinants on the well-being of patients with mental health conditions [15], as shown in Figure 1. The data were collected at a single point in time. This study collected data from the facilities' database and existing health records from the past six years, from 2019 to 2024. At the same time, questionnaires were administered to out-patients in the selected psychiatric facilities. This approach allowed for a snapshot of the associations between various social determinants and the mental well-being of the target population [16]. 2.1. Setting and sample The study setting includes three distinct health facilities in the Philippines: a community-based clinic (Barangay Aurora Hill), a public health office (Baguio City Health Services), and a tertiary hospital (Baguio General Hospital and Medical Center). The study population included out-patients aged 18-80 (and emancipated minors aged 15-17) who had a consultation for a diagnosed mental health condition, including thought, mood, substance abuse, anxiety, neurodevelopmental, cognitive, and eating disorders. Convenience sampling was used to recruit participants. Exclusion criteria included patients in acute psychotic states, inpatients requiring immediate care, individuals exhibiting aggressive or suicidal behavior, vagrants, and those with incomplete health records. All participants were deemed capable of providing informed consent. Figure 1. Retrospective cross-sectional design  ISSN: 2252-8806 Int J Public Health Sci, Vol. 14, No. 4, December 2025: 1886-1896 1888 2.2. Data gathering instruments Two instruments were utilized. Mental well-being was measured using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS), a 14-item scale with established validity and reliability [17], [18]. The WEMWBS has demonstrated high internal consistency (Cronbach's alpha from 0.83 to 0.97) across diverse populations [18]. A self-administered questionnaire was developed to collect data on social determinants of health (SDH), guided by the Lund et al. framework [13], [14]. The questionnaire was structured into proximal and distal factors. To ensure its quality, the instrument underwent a rigorous validation process. Content validity was established through review by three public health experts (content validity index = 0.88). Reliability was confirmed via internal consistency testing (Cronbach's alpha = 0.91), and readability was assessed using the Fog Index (40%). 2.3. Data gathering procedure Ethical approval was secured from Saint Louis University-Research Ethics Committee (SLU-REC 2024-015). Following ethical approval from the university's ethics board, the research team initiated data collection. The process involved two parallel methods: first, extracting anonymized data on social determinants from existing patient databases and electronic health records (EHR) at the facilities; and second, distributing the WEMWBS and SDH questionnaires to eligible outpatients in psychiatric departments. Healthcare staff assisted in identifying and approaching potential participants. 2.4. Data management and ethical considerations Multivariate regression analysis was used to examine the relationship between social determinants (independent variables) and mental well-being scores (dependent variable). All procedures adhered to the ethical guidelines of the Philippine Health Research Ethics Board (PHREB) [15]. Informed consent was obtained from all participants, with materials translated into local dialects to ensure comprehension. Participants were informed of their right to withdraw at any time. To protect confidentiality, all data extracted from databases were de-identified, computer files were encrypted, and access was restricted to the research team. 3. RESULTS Table 1 (see Appendix) presents the demographic, biological, and social characteristics of mental health patients in select facilities within the Philippines. The demographic profile of the 21,813 patient records revealed a population predominantly composed of young adults (46.0% aged 18-30) and females (62.3%), with mood disorders being the most common diagnosis (48.0%). This profile is consistent with general trends in mental health service utilization, where younger individuals and women are often more likely to seek help. The prevalence of mood disorders underscores a significant public health challenge within the studied population. The findings from Table 1 reveal a complex interplay of demographic, biological, social, and economic factors influencing mental well-being among outpatients with mental health conditions. The high prevalence of mood disorders and the potential influence of family history highlight the need to consider both individual and familial factors in mental health care. The significant correlation between birth order and mental well-being suggests that family dynamics and roles may play a role in shaping mental health. The overrepresentation of young adults and females in the sample is consistent with broader trends in mental health service utilization. The assessment of mental well-being, conducted on a subset of 89 survey participants using the WEMWBS, indicated a surprisingly high level of overall well-being, as can be gleaned from Table 2. A striking 12 out of the 14 items on the scale were rated as "High," suggesting a resilient population despite their clinical diagnoses. The item "feeling loved" received the highest mean score (3.93), pointing to the vital role of social connection, while "interest in other people" scored the lowest (mean = 3.30), which, while still in the "Average" range, may signal underlying issues with social engagement or withdrawal. The multivariate regression analysis yielded complex results regarding the predictive power of social determinants, as in Table 1. When analyzed as individual variables, several proximal factors showed a significant correlation with mental well-being, including age (p<0.010), religion (p<0.015), civil status (p<0.001), occupation status (p < 0.037), and illegal drug use (p<0.009). However, a crucial finding emerged when these factors were grouped: the entire block of proximal social factors, while explaining the largest portion of variance in well-being (28.6%), failed to reach statistical significance as a collective predictor (p > 0.574). In stark contrast to the proximal factors, specific distal social determinants demonstrated strong and statistically significant predictive power, as shown in Table 3. The analysis revealed that an individual's birth order (p<0.010), the marital status of their parents (p<0.017), and a history of family sexual abuse (p<0.033) were all significant predictors of current mental well-being. This finding indicates that foundational aspects of an individual's early life and family environment have a more direct and lasting statistical impact on their mental health outcomes than their more immediate social or economic circumstances. Int J Public Health Sci ISSN: 2252-8806  Social determinants in health and mental well-being among out-patients (Rainier C. Moreno-Lacalle) 1889 Table 2. Extent of mental well-being of patients Items None of the time (Count/%) Rarely (Count/%) Some of the time (Count/%) Often (Count/%) All of the time (Count/%) No data (Count/%) Mean Interpretation Optimism about the future 10/11.2% 16/18% 18/20.2% 18/20.2% 15/16.9% 12/13.5% 3.54 High Feeling useful 4/4.5% 15/16.9% 20/22.5% 19/21.3% 18/20.2% 13/14.6% 3.80 High Feeling relaxed 11/12.4% 14/15.7% 25/28.1% 10/11.2% 16/18% 13/14.6% 3.51 High Interest in other people 11/12.4% 18/20.2% 26/29.2% 13/14.6% 9/10.1% 12/13.5% 3.30 Average Energy to spare 10/11.2% 19/21.3% 22/24.7% 17/19.1% 9/10.1% 12/13.5% 3.36 Average Dealing with problems well 8/9% 18/20.2% 22/24.7% 17/19.1% 12/13.5% 12/13.5% 3.48 High Thinking clearly 6/6.7% 17/19.1% 26/29.2% 16/18% 12/13.5% 12/13.5% 3.53 High Feeling good about myself 7/7.9% 17/19.1% 22/24.7% 17/19.1% 13/14.6% 13/14.6% 3.57 High Feeling close to other people 6/6.7% 14/15.7% 26/29.2% 14/15.7% 17/19.1% 12/13.5% 3.65 High Feeling confident 8/9% 19/21.3% 20/22.5% 14/15.7% 16/18% 12/13.5% 3.53 High Able to make up my mind about things 5/5.6% 22/24.7% 17/19.1% 23/25.8% 10/11.2% 12/13.5% 3.53 High Feeling loved 6/6.7% 10/11.2% 2022.5% 13/14.6% 28/31.5% 12/13.5% 3.93 High Interest in new things 9/10.1% 14/15.7% 16/18% 13/14.6% 25/28.1% 12/13.5% 3.75 High Feeling cheerful 11/12.4% 9/10.1% 21/23.6% 13/14.6% 23/25.8% 12/13.5% 3.72 High Table 3. Distal social determinants of mental well-being Distal factors Categories Frequency (%) R pvalue F (R2) p-value Demographic factors Birth order First 23 (26.1%) 0.340 0.001* 4.09 (0.168) <0.010* Second 15 (17.0%) Third 21 (23.9%) Fourth 12 (13.6%) Fifth 5 (5.7%) Seventh 1 (1.1%) Eight 1 (1.1%) No data 10 (11.4%) Clinically diagnosed with a mental health condition Thought disorder 4059 (19.9%) - 0.100 0.351 Mood disorder 9812 (48.0%) Substance abuse 310 (1.5%) Anxiety disorder 757 (3.7%) Neurodevelopmental disorder 440 (2.2.%) Cognitive disorder 49 (0.2%) No data 5013 (24%) Gender Yes 10 (11.4%) 0.029 0.792 No 77 (87.5%) No data 1 (1.1%) Ethno-linguistic membership Yes 11 (12.5%) - 0.175 0.104 No 77 (87.5%) Biological factors 2nd-3rd degree family member/s with mental health condition Yes 23 (25.8%) 0.197 0.064 2.08 (0.046) <0.018* No 65 (73.0%) No data 1 (1.1%) Physical disability Yes 10 (11.2%) 0.112 0.295 No 77 (86.5%) No data 2 (2.2%) Economic factors Job discrimination Yes 12 (13.5%) - 0.101 0.345 0.903 (0.01) <.001* No 74 (83.1%) No data 3 (3.4%) Environmental factors Abuse (physical) Yes 29 (32.6%) - 0.036 0.739 1.84 (0.194) <0.010* No 60 (67.4%) Family attempted to engage sexually with you Yes 9 (10.1%) - 0.226 0.033* No 80 (789.9%) Marital status of parent Yes 15 (21.4%) 0.248 0.017* No 54 (77.1%) No data 1 (1.4%) Social factors Part of an organization Yes 32 (36.0%) 0.120 0.265 1.26 (0.014) <0.001* No 55 (61.8%) No data 2 (2.2%)  ISSN: 2252-8806 Int J Public Health Sci, Vol. 14, No. 4, December 2025: 1886-1896 1890 4. DISCUSSION This study's findings offer a critical, culturally-situated re-evaluation of the proximal-distal framework for understanding social determinants of mental health. The central and most compelling result is the disjuncture between the predictive power of proximal and distal factors: while immediate social circumstances (proximal) collectively explained more variance, it was the foundational, early-life factors (distal) that emerged as statistically significant predictors. This paradox suggests that in a collectivistic, family-oriented culture like the Philippines, the impact of present-day stressors is heavily filtered through the lens of one's established role within the family and formative life experiences [19], [20]. The high self-reported well-being, especially the peak score for "feeling loved," strongly resonates with the core Filipino cultural value of kapwa (shared identity). This concept, which denotes a shared identity and deep interconnectedness, posits the self not as an isolated individual but as inextricably linked to others, particularly family [21]. This powerful cultural script may serve as a significant psychological buffer, allowing individuals to maintain a sense of well-being and belonging even when facing proximal adversities like unemployment or financial instability [22], [23]. This aligns with findings from other Southeast Asian contexts published in this journal, where strong community social support has been shown to be associated with respect and healing [24]. Conversely, the lower score for "interest in other people" may reflect the internal conflict created by mental health stigma. While individuals feel loved and supported within their core unit, the societal stigma associated with their condition can foster social withdrawal and a reluctance to engage with the wider community, a dynamic observed in other socially cohesive Asian cultures [25], [26]. The statistical prominence of distal factors, birth order, parental marital status, and abuse history, underscores the enduring legacy of family structure and adverse childhood experiences (ACEs). In the Filipino context, birth order is not a trivial detail; it often dictates lifelong responsibilities, expectations, and familial roles, thereby shaping an individual's entire life course and stress exposure [27]. The significance of parental separation and abuse aligns with a vast body of global research on ACEs, which robustly demonstrates that early life trauma creates long-term neurobiological and psychological vulnerabilities [28], [29]. Our findings assert that these distal events are not dormant historical data points but are active, potent forces that continue to shape mental health in adulthood. The non-significance of the grouped proximal factors is perhaps the most theoretically challenging finding. It compels a move beyond a simple additive model of social determinants toward one that prioritizes interaction and cultural mediation. For example, the proximal factor of being unemployed may have a vastly different impact on an individual who is the eldest child (panganay), and thus culturally expected to be a primary provider, compared to a youngest child (bunso), who may receive continued family support [30]. This suggests that the predictive power of a proximal factor is contingent upon the distal context in which it occurs. Therefore, a culturally attuned SDH framework must account for how distal factors, like family roles and history, define the meaning and severity of proximal stressors [31], [32]. 5. CONCLUSION This study confirms that social determinants are critical predictors of mental well-being among outpatients in the Philippines, but with a notable cultural nuance. The primary finding is that specific distal factors rooted in family structure and life-course events, such as birth order, parental marital status, and history of abuse—exert a more significant statistical influence on mental well-being than aggregated proximal factors. This highlights the enduring impact of one's developmental and familial context, which appears to mediate the effects of more immediate social and economic conditions in a collectivistic society. These findings have direct implications for public health policy and clinical practice. Interventions should adopt a two-pronged approach: first, by addressing immediate needs through accessible mental health services, but second, and perhaps more critically, by incorporating family-systems therapy and traumainformed care that acknowledge the deep-seated influence of an individual's life history. For future research, longitudinal studies are essential to track the causal pathways between social determinants and well-being over time. Furthermore, qualitative research is needed to explore the lived experiences behind these statistical associations, particularly how cultural values like kapwa (shared identity) and utang na loob (debt of gratitude) function as both protective and stress-inducing factors in the context of mental health. The cross-sectional design prevents the establishment of causal relationships between social determinants and mental well-being, limiting the understanding of how these factors influence each other over time. The sample may not be representative of all outpatients with mental health conditions in the Philippines, as it was drawn from three specific psychiatric facilities only. The reliance on self-reported data may introduce recall or social desirability bias, and the custom SDH questionnaire may not capture all relevant social determinants or cultural nuances. The study may not have fully accounted for all potential Int J Public Health Sci ISSN: 2252-8806  Social determinants in health and mental well-being among out-patients (Rainier C. Moreno-Lacalle) 1891 confounding variables, such as individual resilience and access to mental health services. Finally, the use of multivariate regression analysis assumes a linear relationship between variables, which may not accurately reflect the complex relationship between social determinants and mental well-being. ACKNOWLEDGMENTS This section should acknowledge the esteemed panel members: Dr. Teresa Basatan, Prof. Diadem Depayson, Prof. Sahlee Florendo, and Prof. Mary Rose Valenzuela for their substantial inputs to improve the manuscript. FUNDING INFORMATION Authors state no funding involved. AUTHOR CONTRIBUTIONS STATEMENT This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author contributions, reduce authorship disputes, and facilitate collaboration. Name of Author C M So Va Fo I R D O E Vi Su P Fu Rainier C. MorenoLacalle ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Pilar Bianca Mae G. Ducusin ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Rayvine B. Suma-il ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Lana Stephanie A. Tiu ✓ ✓ ✓ ✓ ✓ ✓ ✓ Kyla Dee Manantan ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Karyle Myara E. Borromeo ✓ ✓ ✓ ✓ ✓ ✓ ✓ Amanda Nicole N. Cruz ✓ ✓ ✓ ✓ ✓ ✓ ✓ Romeo Benedict Landagora ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Chad Allen M. Nerona ✓ ✓ ✓ ✓ ✓ ✓ Christan James R. Edualino ✓ ✓ ✓ ✓ ✓ C : Conceptualization M : Methodology So : Software Va : Validation Fo : Formal analysis I : Investigation R : Resources D : Data Curation O : Writing - Original Draft E : Writing - Review & Editing Vi : Visualization Su : Supervision P : Project administration Fu : Funding acquisition CONFLICT OF INTEREST STATEMENT Authors state no conflict of interest. INFORMED CONSENT We have obtained informed consent from all individuals included in this study. ETHICAL APPROVAL The study was approved by Saint Louis University-Baguio City Research Ethics Committee with an approval number of SLU-REC 2024-015. DATA AVAILABILITY The data that support the findings of this study are available from the corresponding author, [RCML], upon reasonable request.  ISSN: 2252-8806 Int J Public Health Sci, Vol. 14, No. 4, December 2025: 1886-1896 1892 REFERENCES [1] World Health Organization (WHO), “Constitution,” WHO. 2024. [Online]. Available: https://www.who.int/about/governance/constitution [2] E. Kim and B. Lindeman, Wellbeing. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-29470-0. [3] R. Nemati-Vakilabad, P. Mostafazadeh, and A. Mirzaei, “Investigating the impact of organizational justice on the relationship between organizational learning and organizational silence in clinical nurses: A structural equation modeling approach,” Journal of Nursing Management, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/2024/7267388. [4] World Health Organization (WHO), “Social determinants of health,” WHO, 2025. https://www.who.int/health-topics/socialdeterminants-of-health#tab=tab_1 [5] S. N. Gnanapragasam, L. Astill Wright, M. Pemberton, and D. Bhugra, “Outside/inside: social determinants of mental health,” Irish Journal of Psychological Medicine, vol. 40, no. 1, pp. 63–73, Mar. 2023, doi: 10.1017/ipm.2021.49. [6] H. Santamaría-García et al., “The role of social cognition skills and social determinants of health in predicting symptoms of mental illness,” Translational Psychiatry, vol. 10, no. 1, p. 165, May 2020, doi: 10.1038/s41398-020-0852-4. [7] G. Ilgün, “What are the socioeconomic determinants of mental disorders?,” Perspectives in Psychiatric Care, vol. 58, no. 4, Oct. 2022, doi: 10.1111/ppc.13136. [8] A. Frakt, “The proximal/distal paradigm,” Harvard Global Health Institute, 2019. [Online]. Available: https://globalhealth.harvard.edu/the-proximal-distal-paradigm/. [9] Mental Health Foundation, “Poverty: statistics,” Mental Health Foundation, 2023. [Online]. Available: https://www.mentalhealth.org.uk/explore-mental-health/statistics/poverty-statistics. [10] T. S. Alamneh et al., “Socioeconomic inequality in barriers for accessing health care among married reproductive aged women in sub-Saharan African countries: a decomposition analysis,” BMC Women’s Health, vol. 22, no. 1, p. 130, Apr. 2022, doi: 10.1186/s12905-022-01716-y. [11] B. Zegeye et al., “Association between women’s household decision-making autonomy and health insurance enrollment in subsaharan Africa,” BMC Public Health, vol. 23, no. 1, p. 610, Mar. 2023, doi: 10.1186/s12889-023-15434-z. [12] A. B. Martinez, M. Co, J. Lau, and J. S. L. Brown, “Filipino help-seeking for mental health problems and associated barriers and facilitators: a systematic review,” Social Psychiatry and Psychiatric Epidemiology, vol. 55, no. 11, pp. 1397–1413, Nov. 2020, doi: 10.1007/s00127-020-01937-2. [13] C. Lund, S. Stansfeld, and M. De Silva, “Social determinants of mental health,” in Global Mental Health, Oxford University Press, 2013, pp. 116–136. doi: 10.1093/med/9780199920181.003.0007. [14] C. Lund et al., “Social determinants of mental disorders and the Sustainable Development Goals: a systematic review of reviews,” The Lancet Psychiatry, vol. 5, no. 4, pp. 357–369, Apr. 2018, doi: 10.1016/S2215-0366(18)30060-9. [15] Philippine Health Research Ethics Board, “National ethical guidelines for health and health related research,” Philippine Health Research Ethics Board. https://ethics.healthresearch.ph/index.php/phoca-downloads/category/4-neg. [16] M. Setia, “Methodology series module 3: Cross-sectional studies,” Indian Journal of Dermatology, vol. 61, no. 3, p. 261, 2016, doi: 10.4103/0019-5154.182410. [17] B. P. R. Perera, A. Caldera, P. Godamunne, S. Stewart-Brown, A. R. Wickremasinghe, and R. Jayasuriya, “Measuring mental well-being in Sri Lanka: validation of the Warwick Edinburgh Mental Well-being Scale (WEMWBS) in a Sinhala speaking community,” BMC Psychiatry, vol. 22, no. 1, p. 569, Aug. 2022, doi: 10.1186/s12888-022-04211-8. [18] A. Dong et al., “Psychometric properties of the Chinese Warwick-Edinburgh mental well-being scale in medical staff: Crosssectional study,” Journal of Medical Internet Research, vol. 24, no. 11, p. e38108, Nov. 2022, doi: 10.2196/38108. [19] M. Merino, J. F. Tornero-Aguilera, A. Rubio-Zarapuz, C. V. Villanueva-Tobaldo, A. Martín-Rodríguez, and V. J. ClementeSuárez, “Body perceptions and psychological well-being: A review of the impact of social media and physical measurements on self-esteem and mental health with a focus on body image satisfaction and its relationship with cultural and gender factors,” Healthcare, vol. 12, no. 14, p. 1396, Jul. 2024, doi: 10.3390/healthcare12141396. [20] D. M. Frost and D. Castro, “An integrated understanding of the mechanisms linking social stigma to mental health among marginalized populations,” Nature Mental Health, vol. 2, no. 6, pp. 645–654, Jun. 2024, doi: 10.1038/s44220-024-00264-8. [21] J. Macaraeg and C. S. Bersamira, “Intersecting kapwa, resilience, and empowerment: A case study of Filipinos in Hawai‘i during the COVID-19 pandemic,” Asian American Journal of Psychology, Dec. 2024, doi: 10.1037/aap0000348. [22] E. Patmisari, Y. Huang, M. Orr, S. Govindasamy, E. Hielscher, and H. McLaren, “Interventions supporting meaningful connections for people with serious mental illness: a concept-framed systematic narrative review,” Social Psychiatry and Psychiatric Epidemiology, pp. 1–26, Jun. 2025, doi: 10.1007/s00127-025-02812-8. [23] S. L. D. Restubog, A. C. P. Chua, and R. D. D. C. Uy, “The role of familism in the well-being of Filipino university students,” The Asia-Pacific Education Researcher, vol. 29, no. 1, pp. 1–10, 2020. [24] L. H. Nguyen, M. T. Bui, and T. T. Nguyen, “Support group model for Vietnamese people affected by substance abuse and HIV/AIDS,” International Journal of Public Health Science (IJPHS), vol. 14, no. 2, pp. 729–739, 2025, doi: 10.11591/ijphs.v14i2.25129. [25] M. D. Blinka et al., “‘I make myself get busy’: Resilience and social connection among low‐income older adults living in subsidized housing,” Journal of the American Geriatrics Society, vol. 72, no. 10, pp. 3188–3199, Oct. 2024, doi: 10.1111/jgs.19069. [26] T. K. Oswald et al., “Interventions targeting social determinants of mental disorders and the sustainable development goals: a systematic review of reviews,” Psychological Medicine, vol. 54, no. 8, pp. 1475–1499, Jun. 2024, doi: 10.1017/S0033291724000333. [27] Y. Xu, N. Zhan, D. Zhang, Z. Xie, G. Li, and H. Huang, “The effect of sense of security on job performance of medical staff: the mediating effect of psychological capital,” Frontiers in Psychology, vol. 15, Apr. 2024, doi: 10.3389/fpsyg.2024.1347783. [28] V. J. Felitti et al., “Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults,” American Journal of Preventive Medicine, vol. 14, no. 4, pp. 245–258, May 1998, doi: 10.1016/S0749-3797(98)00017-8. [29] R. F. Anda et al., “The enduring effects of abuse and related adverse experiences in childhood,” European Archives of Psychiatry and Clinical Neuroscience, vol. 256, no. 3, pp. 174–186, Apr. 2006, doi: 10.1007/s00406-005-0624-4. [30] M. R. M. Hechanova and B. N. Teh, The way we heal: Stories of recovery from mental illness. Ateneo de Manila University Press, 2022. [31] M. Alegría, A. NeMoyer, I. Falgàs Bagué, Y. Wang, and K. Alvarez, “Social determinants of mental health: Where we are and where we need to go,” Current Psychiatry Reports, vol. 20, no. 11, p. 95, 2018, doi: 10.1007/s11920-018-0969-9. [32] C. G. Coll, E. L. Marks, and J. B. Garcia, The Oxford handbook of culture and development. Oxford University Press, 2023. Int J Public Health Sci ISSN: 2252-8806  Social determinants in health and mental well-being among out-patients (Rainier C. Moreno-Lacalle) 1893 APPENDIX Table 1. Prediction of proximal social determinants on mental well-being Proximal factors Categories Frequency (%) R p-value F (R2) p-value Demographic factors Age ≤ 17 years old 2379 (10.9) 0.348 <0.001* 3.53 (0.147) <0.010* 18-30 years old 10075 (46.0) 31-50 years old 6886 (31.4) 51-70 years old 2214 (10.1) 71 and above 327 (1.5) No data 17 (0.1) Gender Female 13635 (62.3) 0.041 0.704 Male 8251 (37.7) LGBTQIA+ 4 (0.01) No data 6 (0.01) Religion Roman Catholic 49 (55.1) 0.257 0.015* Christian 9 (10.1) Born Again 9 (10.1) Iglesia ni Cristo 2 (2.2) Jehova’s Witness 4 (4.5) No data 7 (7.9) Ethnicity Igorot 30 (33.7) -0.038 0.724 Tagalog 26 (29.9) Ilocano 18 (20.7) Pangasinense 3 (3.4) Ilonggo 2 (2.3) Others 10 (11.2) Biological factor First degree family member/s with a mental condition Yes 19 (21.3) -0.011 0.0921 1.81 (0.197) <0.001* No 69 (77.5) No data 1 (1.1) Economic factors Access to mental health services Yes 51 (57.3) -0.036 0.741 0.656(0.102) 0.037* No 36 (40.4) No data 2 (2.2) Occupation status Employed 21 (23.6) 0.222 0.037* Unemployed 48 (53.9) Self-employed 11 (12.4) Retired 3 (3.4) No data 6 (6.7) Time that you have been working (employed and self-employed) Less than 6 months 9 (29.0) 0.097 0.581 Less than a year 2 (6.4) More than a year 20 (64.5) Environmental factors Abuse (verbal) from the family Yes 48 (53.9) 0.154 0.151 1.01 (0.0345) <0.001* No 41 (46.1) Attempted to selfharm within the last 12 months Yes 32 (36.0) 0.125 0.241 No 56 (62.9) No data 1 (1.1) Living with someone with a drug/alcohol addiction Yes 31 (34.8) 0.005 0.966 No 58 (62.9) No data 2 (2.2) Social factors Alcohol drinker Yes 35 (39) 0.138 0.266 0.876 (0.286) 0.254 No 49 (55.1) No data 5 (5.6) Civil status Single 56 (62.9) 0.389 <0.001* Married 21 (23.6) Widowed 1 (1.1) Separated/annulled 3 (3.4) Cohabitation 1 (1.1) No data 7 (7.9) Drugs (illegal) use Yes 7 (7.9) 0.276 <0.009* No 75 (84.3) No data 7 (7.9) Frequency of communicating with others Less than once a week 15 (16.9) 0.031 0.770 1 or 2 times a week 20 (22.5) 3 to 5 times a week 46 (51.7) No data 8 (9.0)  ISSN: 2252-8806 Int J Public Health Sci, Vol. 14, No. 4, December 2025: 1886-1896 1894 Table 1. Prediction of proximal social determinants on mental well-being (continued) Proximal factors Categories Frequency (%) R p-value F (R2) p-value Social factors House problems Bug infestation 12 (13.5) 0.024 0.823 0.876 (0.286) 0.254 Mold 7 (7.9) Lead paint or pipes 1 (1.1) Inadequate heat 4 (4.5) Oven or stove not working 1 (1.1) No or not working smoke detectors 2 (2.2) Water leaks 5 (5.6) Inadequate living space 4 (4.5) None of the above 47 52.8) No data 6 (6.7) Living with Living alone 7 (8.5) 0.056 0.610 Immediate family 61 (68.5) Extended family 4 (4.0) Partner 14 (15.7) Friends 4 (4.0) No data 2 (2.4) Monthly income 3,000-5,000 per month ($51-86) 10 (24.4) 0.021 0.898 6,000-10,000 per month ($102-171) 11 (26.8) 11,000-20,000 per month ($188-343) 9 (22.0) 21,000-30,000 per month ($360-515) 5 (12.2) More than 30,000 per month ($516) 1 (2.4) ≥ 2,999 per month (below 50) 4 (.8) No data 1 (2.4) Specific 1st degree family member/s with a mental conditions Mother 6 (31.6) 0.257 0.287 Father 2 (10.5) Children 1 (5.3) Sibling 7 (36.8) Mother + Children 1 (5.3) Mother + Father + Sibling 1 (5.3) No data 1 (5.3) BIOGRAPHIES OF AUTHORS Rainier C. Moreno-Lacalle is a multi-disciplinary researcher with a Ph.D. in Nursing from Saint Louis University. He has led a project focused on creating a mobile application for indigenous peoples in the Cordillera Administrative Region suffering from depression and suicidal ideation. He is skilled in quantitative and qualitative research methodologies, including data collection, analysis, and interpretation. He has a proven track record of publishing in peer-reviewed journals and presenting at conferences. He is eager to apply his skills to public health system modeling and contribute to research that has the potential to impact public health outcomes. He can be contacted at email: [email protected]. Pilar Bianca Mae G. Ducusin is a fourth-year nursing student that is set to graduate in the Class of 2025 of Saint Louis University under the program, Bachelor of Science in Nursing. Her exposure to psychiatric and community-based nursing has strengthened her advocacy for mental health and well-being causes that led to this research project, which was a product of her research class in her third year of college. In promoting the study, she was able to participate and present in the 22nd ADPCN National Conference for Nursing Students held in Fiesta Pavilion, Manila Hotel, Ermita, Manila for poster presentation. Besides this notable feat, she is enthusiastic in partaking and garnering more similar-like experiences in research presentations and building connections nationwide. As of now, her research interests include nursing education, pediatric nursing, and medical-surgical nursing and wishes to gather more insights and experiences. She can be contacted at email: [email protected].