Mental health literacy among basic healthcare providers and community health volunteers of Lalitpur Metropolitan City, Nepal

Abstract

Background

Mental health literacy (MHL) is particularly essential for primary healthcare providers for the early recognition and management of mental disorders. In Nepal, mid-level healthcare workers and Female Community Health Volunteers (FCHVs) play a vital role in community-based mental health services, yet their MHL remains insufficiently studied. This study assesses MHL among basic healthcare providers and health volunteers in Lalitpur Metropolitan City, Nepal and identifies factors associated with it.

Methods

A cross-sectional study was conducted among 233 healthcare workers, including mid-level healthcare providers, FCHVs, and other community health volunteers. Participants were selected through simple random sampling. MHL was measured using the Mental Health Literacy Assessment Scale (MHLAS), with scores dichotomized at the median (≥ 75 = high MHL, < 75 = low MHL). Descriptive statistics summarized key variables, while Pearson’s chi-square test and odds ratios (OR) with 95% confidence intervals (CI) were used to identify associations.

Results

The MHLAS score ranged between 45 and 98, with a mean score of 76 ± 9.27 and a median of 75 (IQR: 11). Out of 233 participants, 129 (55%; 95% CI: 49–62%) were classified as having high MHL. Higher education levels (OR = 9.77, p < 0.001) and mid-level healthcare provider status (OR = 2.44, p = 0.020) were significantly associated with higher MHL. Participants with 5–10 years of experience (OR = 4.50, p = 0.029) and those who received mental health training during academic courses (OR = 1.99, p = 0.022) or on the job (OR = 1.90, p = 0.036) had significantly higher MHL. However, factors such as gender, marital status, and work experience beyond 10 years were not significantly associated with MHL.

Conclusion

Despite their critical role in community mental health, nearly half of the primary healthcare providers in Nepal had low MHL. This study highlights the urgent need to integrate MHL training into academic curricula and ensure continuous professional development among basic health care providers.

Introduction

Mental health literacy (MHL) is a critical subdomain of health literacy that refers to the knowledge and beliefs that aid in recognizing, preventing, and managing mental disorders while promoting positive mental health and help-seeking behaviors [1,2,3]. Over time, MHL has expanded to encompass a broader scope, including recognizing specific disorders, understanding risk factors and causes, knowledge of self-help strategies, the ability to seek relevant information, awareness of professional support, and fostering attitudes that encourage early recognition and help-seeking behavior [4, 5].

Globally, mental health disorders remain a significant public health concern, contributing substantially to disability and disease burden. The Global Burden of Disease estimates reveal that Disability-Adjusted Life Years (DALYs) attributed to mental health disorders increased from 80.8 million in 1990 to 125.3 million in 2019 [6]. By 2030, neuropsychiatric conditions are projected to account for 15% of total DALYs, with depression alone expected to contribute 5.7% [7]. In line with global trends, South Asia has seen a rising burden of mental health disorders, exacerbated by socioeconomic disparities and limited access to care [8, 9]. Nepal also reflects these patterns, with the National Mental Health Survey 2020 reporting that nearly 10.0% of adults had experienced a mental disorder in their lifetime, with 4.3% exhibiting current symptoms. Among adolescents, 5.25% suffered from mental disorders, with 3.9% reporting suicidal thoughts [10]. Numerous community-based studies in Nepal have highlighted the heightened mental health problems among subpopulations, including civil war survivors, earthquake victims, mothers, older adults, and individuals with chronic illnesses. Furthermore, general population groups, such as adolescents, students, and working individuals, report significant rates of anxiety, depression, and suicidal ideation [11,12,13,14,15,16]. These findings underscore an urgent need to address mental health issues in Nepalese communities.

Nepal faces a critical shortage of mental health professionals, exacerbating barriers to mental health service provision. The Health Facility Survey 2021 reported that only 25.2% of health facilities in Nepal provide mental health services, with only 27% having proper guidelines, 16% with recently trained staff, and fewer than half maintaining essential medications [17]. The World Health Organization (WHO) metric for mental health workforce suggests measuring the number of professionals per 100,000 population [18]. However, Nepal has approximately 200 psychiatrists (0.68 per 100,000 people) and around 50 psychiatric nurses (0.17 per 100,000 people), numbers that remain alarmingly low for the country’s population needs [19, 20]. Given this shortage, strengthening the MHL of community-based healthcare workers becomes even more crucial, as they play a pivotal role in bridging the gap between individuals with mental health conditions and the healthcare system. In response to address the service gap, Nepal’s Ministry of Health and Population (MoHP) has implemented the Community Mental Health Care Package 2017 and its successor, the “Khulla Mann” District Mental Health Care Program in 2022. These initiatives aim to integrate mental health services into primary care, improve equity, and enhance training and resources at district and community levels [19,20,21]. Despite these efforts, significant gaps remain in service provision and capacity building, particularly at the community level.

Community health workers, including mid-level healthcare providers, Female Community Health Volunteers (FCHVs), school nurses, and other healthcare personnel, are pivotal in bridging the gap between communities and formal healthcare systems [22,23,24]. In Nepal, FCHVs are local women, typically above 25 years of age, who receive 18 days of basic training covering various primary healthcare topics. While they are crucial in community health outreach, their training programs inadequately address MHL needs [25,26,27]. Similarly, mid-level healthcare providers include health assistants, certified medical assistants, auxiliary nurse midwives, and auxiliary health workers, who are crucial in providing primary healthcare at the community level, but often lack formal mental health training. Nepal has adopted the Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) version 2.0, which includes targeted training modules for FCHVs, para-professional counseling training for health assistants and nurses, and specialized programs for public health professionals focusing on the design and evaluation of community-based mental health initiatives [21, 28, 29]. In 2022, over 584 primary healthcare providers and 938 FCHVs received mental health training through these initiatives [30]. This suggests a small fraction of this workforce has undergone formal mental health education or training, leaving significant gaps in capacity.

Despite these initiatives, there remains a lack of empirical data on the MHL levels of basic healthcare providers and the factors influencing their mental health knowledge and competencies. Understanding the current status of MHL among Nepal’s community-based healthcare workers is critical for designing effective training programs and policy interventions. This study aims to assess MHL among basic healthcare providers and health volunteers in Lalitpur Metropolitan City and identify the key factors influencing their MHL. Findings from this study could offer critical insights to inform policies and training programs aimed at enhancing the capacity of these frontline workers, thereby strengthening Nepal’s community-based mental healthcare delivery system.

Method

Study design and setting

This cross-sectional study is based on the same dataset used for the validation of the Mental Health Literacy Assessment Scale (MHLAS) [31]. While the initial study focused on the development and psychometric evaluation of the tool, the current study investigates the factors associated with MHL among basic healthcare providers and community health volunteers in Lalitpur Metropolitan City. This distinction allows for a deeper understanding of determinants of MHL beyond scale validation. The study population comprised mid-level health service providers (up to the 5 th level of service), FCHVs, and other health volunteers. Based on the public health section of Lalitpur metropolitan city, there are eight basic health centers (BHC), six health posts (HP), and five urban health centers (UHC) within Lalitpur Metropolitan City totaling 456 eligible healthcare workers [32]. Of these 456 healthcare workers, 64 are mid-level health service providers, 187 are FCHVs, and 205 are other health volunteers [32]. The public health section of Lalitpur Metropolitan City provided a comprehensive list of the FCHVs in each ward, as well as other health volunteers and mid-level health service providers working across the community health units, including HPs, UHCs, and BHCs, within the metropolitan area. Lalitpur Metropolitan City was selected as the study setting due to its status as one of the most densely populated areas in Nepal, which provides a diverse population of healthcare workers. Additionally, the city is one of the few metropolitan areas where some mid-level health service providers and volunteers have received mental health training, offering a unique opportunity to explore the differences in mental health awareness among health workers who have and have not received such training.

Sample size determination and recruitment

The sample size was calculated using Cochran’s formula for estimating a proportion (n = z²pq/d²). In the absence of prior studies assessing MHL among this population group in Nepal, a prevalence of 50% was assumed to ensure an optimum sample. Using 5% allowable error and 95% confidence interval (CI), and accounting for the finite population of 456 target population, the required sample size was determined to be 209. After adjusting 10% non-response rate, the sample was optimized to 233. Participants were selected using simple random sampling. A list of eligible participants from each group was prepared, and random numbers were generated to select participants. If a selected participant was unavailable, the next eligible participant was selected using the same process.

Data collection

Data were collected via face-to-face interviews from June 8 to 28, 2024. Interviews were conducted in healthcare settings for mid-level health service providers and in private spaces for FCHVs and health volunteers to ensure confidentiality. A structured questionnaire was used, comprising four sections. The first section included sociodemographic characteristics assessing participants’ age, gender, marital status, education level, religion, family type, and ethnicity based on National category [33]. Second section consisted of MHLAS to assess MHL among the participants [31]. The third section included health-related characteristics including past and family history of mental illness, and the presence of chronic diseases. The fourth section covered job-related characteristics including years of work experience, mental health training, academic exposure, and experiences with mental healthcare services. Interviews were conducted after obtaining written informed consent from each participant, and each session lasted 30–45 min.

Outcome variables

The MHL was the outcome variable, assessed using the MHLAS [31], a tool specifically developed for this study. The MHLAS demonstrated strong psychometric properties, including a Cronbach’s alpha of 0.797, indicating good internal consistency. The tool’s development and validation process involved exploratory and confirmatory factor analyses, which identified a robust structure covering multiple dimensions of MHL [31]. This MHLAS consists of 20 items measured on a 5-point Likert scale with response options ranging from ‘strongly disagree’ to ‘strongly agree’. For analysis, MHLAS scores were dichotomized using the median-based cutoff.

Statistical analysis

The data were entered using EpiData 3.1 and exported to Statistical Package for the Social Sciences version 26 for statistical analysis. Descriptive statistics, including frequencies, percentages, and means were calculated to summarize participant characteristics and key variables. Pearson chi-square test was applied to assess the association between different independent variables and MHL at 95% CI and 5% level of significance. Unadjusted odds ratios (uOR) with 95% CI were used as the primary measure of association to explore relationships between independent variables and MHL without introducing assumptions about confounding effects or risking model overfitting, given the exploratory nature of the study and sample size considerations. However, to ensure transparency and robustness, we conducted supplementary analyses treating MHLAS as a continuous variable using linear regression. These supplementary results are included in the additional materials (Supp 1).

Ethical consideration

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Ethical approval was obtained from the Institutional Review Committee of CiST College (Ref No. 15/080/081), and permission was acquired from the Lalitpur metropolitan city. Written informed consent was secured from each participant prior to data collection. Participants were informed of their voluntary participation and their right to withdraw from the study at any time without any consequences. All interviews were conducted in private settings, with mid-level health service providers interviewed in designated areas within health facilities and Female Community Health Volunteers (FCHVs) and other health volunteers interviewed in separate, quiet locations. No personally identifiable information was recorded, and participant responses were anonymized. Data were securely stored in password-protected files, accessible only to the research team. Following data collection, participants were provided with a brief orientation session to promote mental health awareness. The session focused on addressing misconceptions and confusion raised by participants while responding to the MHL tool, particularly explaining reverse-coded items and clarifying common misunderstandings about mental health concepts. This was conducted respectfully and only after completing the data collection process to ensure participants’ responses remained unbiased.

Results

In this study, the age of the participants ranged from 20 to 60 years, with a mean age of 44 ± 7.54 years. Majority of the participants belonged to the 45–55-year age group (44.2%). The study population was predominantly female (85.0%) and primarily belonging to Janajati ethnicity (82.4%), which included Newar, Gurung, and Magar ethnic groups. Regarding education, more than one-third (36.5%) had completed secondary level of education, while 20.6% had graduation or higher qualifications. In terms of occupational roles, more than half were FCHVs (57.5%), while around one-fourth (26.6%) were mid-level health service providers. A majority (82.8%) of the participants had more than 10 years of experience in the health sector, and three-quarters (75.5%) had worked in their current role for over a decade (Table 1).

Table 1 Socio demographic characteristics (n = 233)

Mental health-related exposure and experience among the participants appeared limited. Among the total participants, 7.7% had a past history of mental illness, and 6.9% reported a family history. Most participants (71.2%) reported no morbidity, and only 5.6% reported multiple morbidities. While only 3% had ever worked in a mental health-related organization, 28.8% had received mental health-related sessions during their academic courses, and 27% had undergone mental health training while on their jobs. Despite a low number of participants reporting to have received training, nearly half (47.6%) had had experience handling individuals with mental health problems (Table 2).

Table 2 Occupational and personal characteristics of the participants (n = 233)

The MHLAS scores ranged from 45 to 98, with a mean of 76 ± 9.27 and a median of 75 (IQR: 11). The detailed distribution of MHLAS scores and the supplementary regression results are provided in the supplementary material (Supp 1). Based on this observed median score, participants with MHLAS scores ≥ 75 were categorized as having high MHL, while those with scores < 75 were classified as having low MHL. A total of 129 participants (55.4%, 95% CI: 48.98 − 61.75%) demonstrated high MHL, while 104 participants (44.6%, 95% CI: 38.25–51.02%) exhibited low MHL. It was observed that nearly half (40.3%) agreed that emotional breakdown is a sign of personal weakness, though 30% disagreed. A large majority expressed comfort in supporting a friend with a mental disorder (92.8%), and 82.4% acknowledged the positive impact of regular exercise on mental well-being. Similarly, 82.0% identified depression as a condition often associated with low mood. Nearly all of the participants (97.9%) felt confident suggesting appropriate care for a friend with a mental disorder, and 12.4% incorrectly believed mental disorders affect behavior but not feelings (Table 3).

Table 3 Participants response in mental health literacy assessment questionnaire

MHL was significantly associated with ethnicity and education. Participants belonging to Brahmin/Chhetri ethnicity demonstrated higher odds of good MHL (OR = 2.562, p = 0.014) compared to those belonging to Janajati ethnicity. Similarly, educational attainment beyond secondary school positively influenced MHL. Those with graduation or higher qualifications had significantly higher odds of good MHL (OR = 9.771, p < 0.001) compared to those with primary level of education. Other socio-demographic factors, including age, gender, marital status, and family type were not significantly associated with MHL (Table 4).

Table 4 Sociodemographic factors associated with mental health literacy

In terms of occupational factors, mid-level health service providers were more likely to have higher MHL (OR = 2.444) compared to other health volunteers. Additionally, participants with 5 to 10 years of experience in the health sector were significantly more likely to have higher literacy (OR = 4.500) compared to those with less than five years of experience. Receiving mental health-related sessions during academic courses (OR = 1.997, p = 0.022) and training while on the job (OR = 1.908, p = 0.036) were also significantly associated with higher MHL. However, factors such as morbidity status, past or family history of mental illness, and experience handling individuals with mental health issues did not exhibit significant associations with MHL (Table 5).

Table 5 Occupational factors associated with mental health literacy

Discussions

This study aimed to assess MHL among basic healthcare providers and health volunteers in Nepal. The findings highlighted that more than half of the participants demonstrated high MHL, with significant associations observed between MHL and factors such as educational attainment, ethnicity, occupational role, and prior mental health training. These findings underscore the need to prioritize initiatives to improve MHL among frontline healthcare providers, as they play a crucial role in delivering community-based mental health services. To our knowledge, there are no studies in Nepal that have assessed MHL among healthcare providers which could be compared with the findings of this study. However, a study conducted among undergraduate medical students in eastern Nepal revealed a higher MHL rate (74%) among them [34], suggesting that formal medical education may contribute to greater MHL levels. Likewise, a university-based study found that 60.8% of undergraduate students at Tribhuvan University, Nepal had limited health literacy [35], reinforcing the need for targeted MHL interventions. The lack of prior research on MHL among healthcare providers in Nepal underscores the novelty of this study and highlights an existing gap in public health research.

In global context, international studies have reported varying levels of MHL among healthcare providers. The level of MHL observed in this study is comparable to findings from high-income settings such as Australia and the UAE, where primary healthcare workers demonstrated moderate-to-high level of MHL [36, 37]. In Australia, a study among nursing students found that structured mental health education contributed significantly to higher MHL levels, emphasizing the role of formal training in improving mental health knowledge and confidence in clinical practice [36]. Similarly, in the UAE, a study among pediatric hospital staff reported moderate levels of MHL and also highlighted cultural factors as key influences on MHL and help-seeking behaviors, as nearly 80% of staff reported reading religious texts as a form of psychotherapy, and nearly 63% endorsed prayer sessions with religious leaders as a treatment approach [37]. These findings highlight the importance of structured training and cultural considerations, reinforcing the need for targeted MHL interventions in Nepal. In contrast, a systematic review of seven cross-sectional studies based on healthcare providers in Arab Gulf States found generally low levels of MHL [38], suggesting significant regional disparities. The disparity in MHL across different studies may be attributed to variations in study settings, population characteristics, and differences in measurement tools.

In this study, no statistically significant association was found between age and MHL. A similar observation was made in a cross-sectional survey conducted in Nigeria among undergraduate students, where age was not significantly associated with MHL [39]. Likewise, in regard to gender, a university-based study in Nepal found that females were 1.6 times more likely to have limited health literacy than males [35]. Unlike prior studies that suggested gender differences in health literacy, no significant gender differences in MHL were observed in this study. This may be due to the underrepresentation of males in the sample, as male mid-level healthcare providers and health volunteers were fewer in number in Lalitpur Metropolitan City. It was observed that ethnicity of the participants was significantly associated with their level of MHL, with Brahmin/Chhetri participants demonstrating higher MHL levels than Janajati participants. This aligns with findings from a systematic review examining the relationship between health literacy and health disparities, which reported that ethnicity plays a crucial role in health literacy outcomes [40, 41]. While our study did not specifically assess the underlying reasons for these disparities, they may be partially influenced by differences in educational opportunities, socioeconomic status, and cultural beliefs regarding mental health across ethnic groups. Future research should explore these potential contributing factors in more detail.

Education was observed as one of the key determinants of MHL in this study. Participants with secondary education (uOR: 2.842, p = 0.027) and graduate-level qualifications (uOR: 9.771, p < 0.001) had significantly higher MHL compared to those with only primary education. This finding is consistent with the finding of a study from China where participants with higher education levels had significantly higher score in mental health knowledge questionnaire (MHKQ scores) (F = 65.72, P < 0.001) [42]. Similar findings were shared by studies from South Africa and Zambia [43], Turkey [44], Australia [45], and Saudi Arabia [37], where higher education levels correlated with greater MHL. For instance, a study conducted in South Africa and Zambia found that primary healthcare workers with higher educational attainment exhibited a greater ability to recognize mental health disorders [43]. Additionally, mid-level healthcare providers were twice more likely to have better MHL (uOR: 2.444, p = 0.042) compared to health volunteers, likely due to greater exposure to professional healthcare settings and structured training.

Interestingly, participants with 5 to 10 years of experience demonstrated significantly higher MHL (uOR: 4.500, p = 0.029) than those with less than five years of experience. However, those with more than ten years of experience showed slightly lower MHL levels. This suggests that prolonged experience alone does not necessarily equate to updated knowledge or skills in mental health. Without continuous professional development, healthcare providers may rely on outdated practices and miss advancements in mental health care. Similar trends were reported in Indonesia and Pakistan, suggesting the role of experience in health literacy but the prolonged experience alone does not necessarily translate into improved MHL unless the healthcare providers are provided with proper training and exposures [46, 47], possibly due to outdated training, lack of continued professional development, or limited exposure to emerging mental health concepts. Receiving mental health-related sessions during academic courses was also associated with higher MHL among the basic healthcare providers and health volunteers. This is supported by a study on traditional healers in Northeast Ethiopia, which found that mental health training significantly increased MHL scores [48]. These findings underscore the necessity of integrating periodic mental health training into professional development programs, ensuring that healthcare providers remain well-equipped to address evolving mental health challenges.

Despite the significant role of basic healthcare providers in Nepal’s mental healthcare system, only around one-fourth of the participants had attended MHL-related sessions during their education, and a similar proportion had received job-related mental health training. It was also observed that despite many basic healthcare providers having 27 to 30 years of experience in the health sector, a considerable proportion still lacked adequate MHL. This gap may be attributed to the absence of structured mental health-related sessions in academic curricula. Globally, similar deficits in mental health training among primary healthcare workers have been reported in other low- and middle-income countries (LMICs) such as India [49], South Africa and Uganda [50], and Pakistan [47], where healthcare providers often lack formal mental health education, limiting their ability to deliver effective mental health services. This reinforces the urgent need to integrate structured mental health training into both academic curricula and professional healthcare settings. The WHO’s Mental Health Gap Action Programme (mhGAP) has emphasized the importance of enhancing the skills of existing healthcare providers through continuous training to improve MHL and service delivery [51]. In Nepal, the National Health Training Center (NHTC), under the Ministry of Health and Population (MoHP), is responsible for coordinating training programs for various healthcare providers, including mental health training modules for primary healthcare workers and FCHVs [52]. However, implementation challenges remain, including insufficient prioritization of mental health in public health policies, inadequate funding, and poor integration of mental health into primary care services [53, 54]. Strengthening these training initiatives through government support, funding allocation, and integration of mental health education into existing healthcare training programs is crucial for addressing these gaps.

The findings of this study have important implications for mental health education and policy development in Nepal. Strengthening MHL among basic healthcare providers and health volunteers through structured training and continuous professional development is crucial for improving community-based mental health services. This study’s strengths lie in its use of a validated tool specifically designed for basic healthcare providers and health volunteers, ensuring contextual relevance in the Nepalese healthcare setting. Several measures were taken to minimize potential biases, such as ensuring a private and comfortable environment for participants to provide honest responses and incorporating a validated tool tailored for healthcare providers.

Despite the strengths of this study, there are certain limitations. The self-reported nature of the data may have led to social desirability bias, despite efforts to assure anonymity and confidentiality. The use of a median-based cutoff for categorizing MHLAS scores, while justified based on the non-normal distribution of the data, may not be the most generalizable approach. Future studies should consider alternative classification methods and validate different threshold values to ensure robustness in diverse populations. Although uOR were assessed to examine associations between MHL and various sociodemographic and occupational factors, more robust analyses, such as multivariate logistic regression or other advanced statistical modeling techniques, were not performed due to the relatively small sample size, which could increase the risk of overfitting and reduce statistical reliability. Future studies with larger sample size could incorporate these methods to better account for potential confounders and provide a more comprehensive understanding of the determinants of MHL. Additionally, responses related to the history of mental illness and their ability to handle and guide mental health patients were self-reported, which could introduce information bias, even though participants were encouraged to respond honestly. Although the study included participants with diverse healthcare backgrounds, work experience, and levels, its scope was limited to an urban setting, which may not fully capture the experiences of healthcare providers in more rural or remote areas. Future studies should use a mixed methods approach to examine MHL disparities in this target group of population across varied geographical settings and evaluate the effectiveness of ongoing training initiatives to inform national mental health strategies. Additionally, longitudinal studies are recommended to assess the long-term impact of mental health training on healthcare providers’ knowledge, attitudes, and clinical practices.

Conclusion

This study provides valuable insights into the MHL of basic healthcare providers and health volunteers in Lalitpur Metropolitan City, Nepal, highlighting the significant influence of education and training on MHL levels. The findings emphasize the need for targeted interventions, such as structured training programs and continued professional development initiatives to enhance MHL among the healthcare workers. Understanding how MHL evolves over time can provide valuable insights into the effectiveness of training interventions and guide evidence-based policymaking for sustained mental health capacity building in Nepal.