The global surge in mental illness is a growing concern, with less than 10% of individuals in many countries receiving adequate mental health treatment. This alarming increase encompasses a wide range of conditions, from common disorders like depression, anxiety, and schizophrenia, to rarer ones such as bipolar disorder(1). The World Health Organization has projected that depression will become the leading cause of disease burden worldwide by 2030, underscoring the worrisome rise of depressive disorders(2). Depression, a condition that can affect individuals from all walks of life, is highlighted as a pervasive concern(3). Research conducted by Bromet et al.(4) revealed a one-year prevalence of major depression at 5.5% across 18 countries, while Lim et al.’s systematic review(5) identified a global prevalence of 7.2%, with notably higher rates in women and countries with medium human development indexes. The increase in mental health issues is especially pronounced among university students worldwide, as evidenced by the previous studies, all of which highlight a significant rise in both prevalence and severity over the last decade(6-10). In Southeast Asia, Tran et al.’s meta-analysis(11) in Vietnam, found a prevalence of depression at 14.6%, with notably higher rates among health workers. Similarly, Pham et al.(12) reported a prevalence of self- reported depression at 15.2% among medical students in Vietnam. The lack of awareness surrounding mental health is also a significant issue, as demonstrated in the Philippines(13). The study of Siriraj Hospital that included 148 Thai medical students found that the prevalence of depression evaluated from the Patient Health Questionnaire (PHQ-9) was 19.6%(14). This situation can be linked to a widespread lack of awareness regarding mental health, as highlighted(15). For Thailand, the prevalence of depression exceeds 5%(16,17), surpassing the global average(2), which estimates that 4.4% of the worldwide population, or approximately 322 million people, suffer from depression. Previous studies in Southeastern-Asian populations exhibited a modest level of awareness regarding depression disorders, a significant prevalence of stigmatizing beliefs, and a limited inclination to seek assistance from mental health professionals(18).
Mental health literacy (MHL) has emerged as a pivotal factor in reducing the risks of depression within communities, drawing the attention of mental health researchers worldwide. MHL plays a significant role in influencing help-seeking behavior and empowering communities to take preventive actions, thereby enhancing the quality of life and well-being of students and the wider population(19,20). This growing consensus acknowledges the positive impact of MHL in addressing mental health, major depression, and behavioral health issues(21-23). Lack of improved relationship quality. These findings lead to the hypothesis that positive psychology may affect helping behaviors among health volunteers, a pertinent consideration in the digital age(28,29).
Helping behavior is defined as actions aimed at assisting or alleviating the distress of individuals such as casual and substantial helping, emotional support, and emergency assistance(30,31). It differs from “prosocial behavior”, which includes any friendly actions, and “altruistic behavior”, driven by a primary concern for another’s well-being(30). Various theoretical perspectives such as evolutionary, genetic, cultural, and psychological explanations, explore the origins of helping behavior(32,33). Engaging in helping behaviors offers both external and internal benefits, particularly in depression recovery(34). Externally, it enhances social relationships, fosters cooperation, and community care(32). Internally, helping behavior provides mental health advantages and aids in alleviating depression(34). Previous studies found that high prosocial behavior levels correlate with reduced adolescent anxiety symptoms(35), helping behavior reduces major depressive disorder among adults, with gender differences noted(36), awareness, understanding of mental health problems, and links between helping behavior and and delayed treatment due to familial ignorance contribute to the severity of symptoms in individuals with mental health issues(24). Jorm et al.(25) introduced the concept of MHL, extending the term health literacy (HL), defining it as “knowledge and beliefs about mental disorders that aid their recognition, management, or prevention.” MHL encompasses recognizing specific disorders, knowledge of risk factors and causes, understanding self-help interventions, awareness of available professional help, fostering attitudes conducive to recognition and help-seeking, and knowledge of where to access mental health information.
The original MHL assessment, introduced by Jorm et al.(25,26), employed vignette interviews to explore participants’ understanding of individuals facing mental health difficulties, negative effect(37), positive effect, and life satisfaction(38,39). In summary, helping behaviors play a vital role in promoting mental health, especially in the context of depressive disorders.
The present study addressed a substantial gap in global MHL research, particularly concerning anxiety disorders, given the limited available studies(40,41). Existing investigations on HL and MHL primarily rely on Western models, lacking specific assessments for Thai university students. Surveys conducted in Thailand in 2014 and 2016 revealed concerning levels of inadequate HL, with 59.4% and 49.0% of adults, respectively, lacking sufficient HL(42). These figures mirror global trends, with 32.5% of the U.S. population demonstrating low HL(43). As a result, the However, this approach specific present study aimed to develop an MHL and MHL subscales and practicality for researchers and practitioners. Moreover, research by Intarakamhang and Macaskill(27) indicated that positive psychology and HL significantly influenced health behavior. However, the impact of positive psychology on behavioral outcomes, particularly helping behaviors, remains an area with limited evidence. Studies suggest that participants engaged in gratitude and enjoyed behavior scale. The research hypothesis, measurement model, and causal relationship model align well with empirical data, offering valuable insights.
Materials and Methods
This cross-sectional study investigated the link between MHL’s causal model and the behavior of people at risk of depression using empirical data between March and September 2023. The study focused third to sixth-year medical science students, categorized by field as medicine, nursing, psychology, and physical therapy, who willingly took part in all studies. The sample size was calculated to meet statistical guidelines, exceeding the recommended 200 individuals for confirmatory factor analysis (CFA)(44), and 10% of the sample size for missing protection. A sample size of 220 was required through stratified random sampling.
Data collection
Amid the COVID-19 pandemic, the research team collected data online. They obtained permission and scheduled meetings with deans, faculty, and student leaders in various branches. Working with a research assistant and local representatives, they explained the study’s goals and guided participants completing surveys via Line groups. The researchers followed ethical guidelines and secured approval (SWUEC/E, No. 055/2566E) from the University’s Institutional Review Board (IRB). The researcher efforts to protect participant anonymity included several implementations. First, before data collection, the authors gained ethical approval, signifying that the ethical review board thoroughly evaluated and sanctioned the methodology. Furthermore, participants were supplied with thorough information regarding confidentiality methods, including the use of pseudonyms, the secure management of data, and a thorough informed consent process. These measures were designed to protect the privacy of the participants and maintain the ethical standards of the present study.
Instruments and quality assessment
A five-point Likert scale with 1=not true at all to 5=absolutely true, assessed questionnaire items, based on operational definitions from conceptual models. Measuring tools included 1) MHL, adapted from Jorm et al.(25) and O’Connor & Casey(45), covering knowledge in five areas, and 2) helping behavior(36,46), encompassing two perspectives. The content’s validity was leveraged by conducting a comprehensive examination and selecting items based on their relevance to the constructs being studied. Furthermore, a preliminary study was performed to determine the credibility of the present study tools. The feedback obtained in the pilot study enabled the authors to enhance and optimize the precision and suitability of the research measurement instruments. Validation achieved the content validity index (CVI) of 0.67 to 1.00 with three experts(47). Testing with 30 participants similar to the study group yielded Cronbach’s alpha of 0.811 to 0.901 and item-total correlations of 0.252 to 0.828(48).

Data analysis
Descriptive statistics, such as means and standard deviations, were used to examine key data aspects by IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA). CFA assessed the measurement model’s consistency with empirical data, and Structural Equation Modeling (SEM) analyzed the causal relationship model’s fit. Model fit was evaluated using benchmarks including a statistically significant chi-square (χ²), χ²/df ratio less than 5, root mean square error of approximation (RMSEA) of 0.08 or lower, standardized root mean square residual (SRMR) less than 1.00, comparative fit index (CFI) greater than 0.90, goodness of fit index (GFI) greater than 0.90, and normed fit index (NFI) greater than 0.90, as outlined in reference(44). Data analysis utilized LISREL version 8.72.

Objective: To develop an MHL and helping behavior (HB) scale while examining a causal relationship model of HB. Materials and Methods: The present study was a cross-sectional study designed by collecting 211 Thai health science students through stratified random sampling. The development process and quality assessment of the MHL and HB measures revealed five components by trying out 30 participants with Cronbach’s alpha of 0.811 to 0.901 and item-total correlations of 0.252 to 0.828. Data were analyzed by confirmatory factor analysis (CFA) and structural equation modeling (SEM) utilizing LISREL version 8.72.
Results: The data of the online questionnaire collection from 211 Thai health science students were complete and clean. They were predominantly females at 80.57%, males at 16.11%, and others or not specified at 3.32%. They were distributed across faculties with Faculty of Health Sciences/Psychology at 29.38%, Faculty of Physical Therapy and Public Health at 26.07%, Faculty of Nursing at 25.12%, and Faculty of Medicine at 19.43%. In terms of the year, 52.13% were in the third year, 27.93% in the fourth year, 15.17% in the fifth year, and 4.77% in the sixth year. Regarding financial status, 47.87% had adequate finances or no savings, 37.44% had sufficient funds with savings, 11.37% faced financial insufficiency or no debt, and the other 3.32% did not specify. Results of hypothesis test: 1) CFA confirmed the construct validity of both measures with empirical data. The 15-item MHL scale achieved an overall Cronbach’s alpha of 0.84 and factor loading between 0.35 and 0.85. Similarly, the 17-item HB scale achieved an overall Cronbach’s alpha of 0.86 and factor loading between 0.30 and 0.79, and 2) the causal relationship model estimation demonstrated a significant positive direct influence of MHL on HB that MHL could predict HM by 67.00%.
Conclusion: Both concise measurement scales exhibited good quality, making them viable options for use by healthcare professionals or educators in screening and assessing students’ abilities in aiding individuals at risk of depression.
The researchers expanded their study to include university students working in healthcare professions, utilizing adjusted MHL and helping behavioral scales to evaluate their MHL and helping behavior. In line with the research hypothesis, both the measurement and causal relationship models exhibited a good fit with the empirical data. These findings have implications for future research, such as using the developed scales to predict helping behavior improvements in related fields.