Investigating the determinants of mental health literacy in school students: a school-based study
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Zeinab Habibpour
,
Mohammad Saadati
Abstract
Objectives
Mental health literacy (MHL) plays a crucial role in promoting mental health and early identification of psychological issues among students. Identifying the determinants of MHL can contribute to the development of effective educational interventions and policies. This study aimed to investigate the factors influencing MHL among high school students in Khoy, Iran.
Methods
This cross-sectional study was conducted on 564 high school students selected through a two-stage cluster sampling method from six health-promoting schools in Khoy. Data collection tools included a demographic questionnaire and a standardized mental health literacy questionnaire. Data were analyzed using MANOVA, Pearson correlation, and multiple linear regression tests. This project has an ethics approval code.
Results
The results indicated that male students had significantly lower mental health literacy (MHL) scores compared to female students (β=−3.51, p<0.001). Maternal education and occupation were identified as significant predictors, with students whose mothers had university education (β=−5.64, p=0.03) or were employed (β=−2.33, p=0.02) achieving higher MHL scores. Similarly, students with employed fathers also scored higher (β=−1.92, p=0.03). No significant associations were found between MHL and economic status, living situation, or parental age.
Conclusions
Based on the findings of the study, gender, maternal education, and parental occupation significantly influence students’ mental health literacy (MHL). Therefore, it is recommended that educational interventions and targeted strategies be implemented to enhance MHL, with a particular focus on male students and those from families with lower educational and occupational status. Such efforts should aim to address existing disparities and promote equitable access to mental health resources and knowledge.
Introduction
One of the key indicators for assessing the health of various societies is the mental health and well-being of that community. Mental health plays a crucial role in ensuring the dynamism and efficiency of any society [1]. Adolescent students, as an active segment of society, face new roles, increased academic responsibilities, and life challenges as they enter this stage. These factors often lead to conflicts, pushing them toward culturally and socially contradictory behaviors and threatening their mental health [2].
Adolescence is often perceived as a stage of life free from health issues [3]; however, compared to children and adults, adolescents are at a higher risk of mental health problems [4]. Numerous reports indicate a significant rise in mental health issues among this population in recent years [5]. In fact, many persistent disorders, such as schizophrenia, major depression, bipolar disorder, substance abuse, and eating disorders, first emerge during this period. If left untreated, these disorders can lead to negative outcomes, including poor academic, occupational, and social functioning, impaired interpersonal and family relationships, and reduced life expectancy [6].
Approximately 10–20 % of children and adolescents worldwide suffer from a mental health issue [3], 7], 8]. In Europe, the prevalence of psychiatric disorders among adolescents is reported at 15.5 %, with nearly one in five young people experiencing a mental disorder, and anxiety disorders having the highest combined prevalence [9], 10].In Iran, about 25 % of adolescents do not have adequate mental health [11]. A national epidemiological study in Iran found that 6,209 out of 30,532 individuals (22.31 %) were diagnosed with at least one psychiatric disorder. The prevalence of psychiatric disorders was significantly lower among girls, rural residents, and 15-year-olds [12]. It is now widely recognized that approximately 70 % of mental disorders can be diagnosed before the age of 25, making them the largest component of the disease burden in the second decade of life [13]. Children and adolescents are the builders of a nation’s future, making their mental health of particular importance [1]. Understanding how various vulnerabilities can place children and adolescents at greater risk of developing mental illnesses, partly due to their neuropsychological immaturity and psychological stressors, should be a priority for this population [14], 15].
Mental Health Literacy (MHL) refers to an individual’s ability to understand, evaluate, and manage mental health issues. This includes recognizing symptoms of mental disorders, accessing supportive resources, and making healthy decisions to improve mental well-being [16] MHL provides the necessary foundation for prevention, care, and mental health promotion, integrating essential components into a unified framework focused on improving mental health outcomes rather than solely promoting well-being [17].
MHL is particularly important for raising awareness about mental illnesses during adolescence, a critical period for the onset of mental disorders. Determining MHL levels guides studies in mental health promotion activities [18]. Many studies have shown that adequate mental health literacy is a key determinant in promoting mental health, especially for individuals with severe depression, a condition often associated with suicide-related outcomes [19], 20]. The mental health of children and adolescents can be improved through routine screenings, increased awareness of mental disorders, and minimizing stigma and socioeconomic inequalities [10]. In a study by Tay Ling et al., the primary factor for the prevalence of psychiatric disorders worldwide was identified as the limited level of mental health literacy in societies [21]. Adolescent mental health can have significant impacts on their overall well-being. Insufficient MHL is also a global concern [11]. As MHL levels decrease, the risk of mental health problems increases [22]. Without a solid foundation in MHL, young people will not be well-prepared to navigate the challenges of adulthood successfully. Recent evidence suggests that improving mental health knowledge and reducing stigma are two essential components for facilitating help-seeking behaviors and early identification of mental disorders [13]. Despite the gradual increase in mental health literacy among the general population, adolescents continue to have moderate to low levels of MHL, which is associated with an increased risk of exacerbating mental disorders [23].
Background
A study in Turkey showed that the level of mental health literacy in high school students was low and they did not seek help from primary care services for mental health problems. The students’ awareness of depression, schizophrenia and social phobia was 28.1 , 46.5 and 5.9 %, respectively. A significant relationship was observed between the level of mental health literacy of students and gender, grade, father’s education level and experience of similar problems [18]. A study by Solhi in Iran found that MHL among adolescents was inadequate, and help-seeking behaviors among Iranian adolescents were below average. Most adolescents tried to hide their mental health issues from others [11]. Given that healthy behaviors and habits are formed during adolescence and that adequate MHL can support informed healthy lifestyles [18], the increasing prevalence of mental health disorders and more severe forms of mental health difficulties is affecting the mental health of younger adolescents [24] Therefore, it is important to improve MHL at an early stage by identifying the determinants of MHL unique to this population group with a specific Iranian culture. Given that low MHL is considered a global public health concern, extensive studies have been conducted in developed countries to identify the determinants of MHL and various MHL programs have been launched at the national level. However, no fundamental measures have been taken in developing countries yet [25] and considering that students are in a critical stage of growth and development, a proper understanding of mental health can have positive impacts on their academic and social performance. Additionally, identifying the factors influencing MHL in this age group can assist policymakers and educators in designing appropriate educational programs and interventions to create supportive and awareness-raising environments in schools. Therefore, this study aimed to investigate the determinants of mental health literacy in students.
Materials and methods
Study design
This cross-sectional study aimed to investigate the determinants of mental health literacy among senior high school students in Khoy, Iran. In Khoy, there are nine health-promoting secondary schools, six of which are senior high schools. A total of 564 secondary high school students were included in the study using a two-stage cluster sampling method.
Sample size and sampling method
The study population comprised high school students in Khoy city. The estimated sample size was calculated based on a pilot study involving 26 students from three randomly selected schools, which yielded a mean mental health literacy score of 70.22 and a standard deviation of 27.85. Using a confidence level of 95 % (α=0.05, Z=1.96) and an estimation error of 3.51 (which is 5 % of the mean), the initial sample size was computed with the formula for estimating the mean. To account for the cluster sampling design and potential intra-cluster correlation, a design effect of 2.5 was applied, resulting in a final sample size of 605 students (n′=242 × 2.5=605).
Regarding the sampling procedure, a two-stage cluster sampling approach was utilized to enhance representativeness. In the first stage, six schools were randomly selected using systematic random sampling from a total of nine health-promoting schools in Khoy. These included three girls’ schools and three boys’ schools to ensure gender representation. In the second stage, within each selected school, students from grades 10, 11, and 12 were included. One class per grade level in each school was randomly chosen, and all students within these classes were invited to participate, resulting in approximately 100 students per school (about 30–35 students per class). The sample was proportionally allocated based on the regional gender distribution of high school students (51 % girls and 49 % boys), leading to the targeted 309 girls and 296 boys. Of the 605 students approached, 564 completed the study, yielding a response rate of 93.2 %.
Data gathering
The data collection tools in this study included a demographic questionnaire for the participants and a mental health literacy questionnaire.
In this study, the inclusion criteria comprised second-level high school students residing in the city of Khoy, who had no history of psychological disorders and provided informed consent to participate in the research. Conversely, the exclusion criterion was defined as incomplete responses to the questionnaires.
Mental health literacy tool
The mental health literacy (MHL) questionnaire was originally developed by Zarebi et al. (2020) for adolescent populations in Iran. It contains 29 items covering four key dimensions: Knowledge of Mental Health Problems, Erroneous Beliefs and Stereotypes, First Aid Skills and Help-Seeking Behaviors, and Self-Help Strategies. Responses are rated on a 5-point Likert scale from “strongly agree” to “strongly disagree,” with total scores ranging from 29 to 145; higher scores indicate greater literacy. The tool’s reliability and validity have been previously established, with a reported Cronbach’s alpha of 0.75 [26].
Given the cultural sensitivity of mental health concepts, the questionnaire was carefully adapted to ensure its cultural relevance for the study population. This process involved several steps: translation and back-translation by bilingual experts to maintain semantic accuracy, review by a panel of mental health professionals familiar with local cultural nuances to ensure cultural appropriateness, and pilot testing with a small sample of high school students similar to the study population to confirm clarity, relevance, and acceptability. Feedback from the pilot informed minor modifications to items to enhance comprehensibility and cultural sensitivity without altering the core content of the original scale. In the pilot phase of the survey, several minor modifications were implemented to enhance clarity and effectiveness. First, some lengthy and complex questions were shortened to ensure that respondents could easily understand what was being asked. Additionally, difficult terminology was replaced with simpler, clearer language that is culturally relevant to the target audience, ensuring that the words resonate with the community’s values and everyday experiences. This approach not only facilitated better comprehension but also fostered a sense of familiarity and comfort among respondents.
Data collection was coordinated with school authorities, and trained researchers visited each class. After explaining the study objectives, the students completed the questionnaires, which took approximately 20 min.
Data analysis
Data were analyzed using SPSS version 19. Descriptive statistics, including means, standard deviations, and frequencies, were used to summarize the data. The Kolmogorov–Smirnov test was employed to assess the normality of the data distributions. To examine the associations between variables, Pearson correlation coefficients were calculated. For inferential analysis, multiple linear regression and MANOVA were conducted. Prior to these analyses, the assumptions for each method were formally checked: multi-collinearity was evaluated via variance inflation factors (VIFs), with all VIFs below 5 indicating acceptable levels; homogeneity of variance-covariance matrices was assessed using Box’s M test for MANOVA, which was non-significant (p>0.05), supporting the assumption; and linearity and normality of residuals were examined through scatterplots and normal P-P plots. Based on these checks, the analyses proceeded appropriately. The significance level was set at 0.05.
Findings
Study population
Table 1 shows demographic data for 564 students (aged 16–19, mean 17.52 ± 0.94 years). Most were male (52.5 %), with 56.4 % from middle-income families. Mothers’ education: 4.3 % uneducated, 16.0 % university-educated. Fathers’ education: 1.2 % uneducated, 21.5 % university-educated. Most mothers were homemakers (91.5 %); 76.8 % of fathers were free workers. The majority (94.1 %) lived with both parents.
The characteristics of study population.
| Variables | n (%), mean ± SD |
|---|---|
| Age | 17.52 ± 0.94 |
| Gender | |
| Male | 296 (52.5) |
| Female | 268 (47.5) |
| Grade | |
| 1 | 184 (32.6) |
| 2 | 193 (34.2) |
| 3 | 187 (33.2) |
| Mother’s education | |
| Uneducated | 24 (4.3) |
| Primary | 107 (19.0) |
| High School | 113 (20.0) |
| Diploma | 230 (40.8) |
| University | 90 (16.0) |
| Father’s education | |
| Uneducated | 7 (1.2) |
| Primary | 108 (19.1) |
| High School | 133 (23.6) |
| Diploma | 195 (34.6) |
| University | 121 (21.5) |
| Mother’s occupation | |
| Household | 516 (91.5) |
| Employed | 48 (8.5) |
| Father’s occupation | |
| Unemployed | 433 (76.8) |
| Employed | 131 (23.2) |
| Economic status | |
| Weak | 43 (7.6) |
| Middle | 318 (56.4) |
| Good | 203 (36.0) |
| Live witha | |
| Parents | 531 (94.1) |
| Other | 33 (5.9) |
| Mother’s age | 42.49 ± 5.78 |
| Father’s age | 48.16 ± 6.05 |
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aLiving with parents refers to living with both father and mother in the same household. Living with others refers to living with only the father, only the mother, or with other guardians.
Mental health reliability assessment, and correlation table
Table 2 presents the means, standard deviations, and reliability indices (Cronbach’s alpha and composite reliability) for the mental health literacy scale and its subscales. All scales showed acceptable internal consistency (α and CR≥0.70). Significant correlations were found between subscales: “Knowledge of Mental Health Problems” strongly correlated with “Erroneous Beliefs and Stereotypes” (r=0.81, p<0.01), indicating greater knowledge was associated with fewer misconceptions. “Self-Help Strategies” correlated moderately with both “Knowledge” (r=0.56, p<0.01) and “Erroneous Beliefs” (r=0.35, p<0.01). “First-Aid Skills and Help-Seeking Behavior” also showed significant correlation with “Knowledge” (r=0.55, p<0.01).
The Mean and standard deviation of the mental health literacy, its reliability assessment, and correlation table of MHL subscales.
| Variables | No of items | Mean | SD | Cronbach’s alpha | CRa | Correlation table1 2 3 4 | |||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | ||||||
| 1. Mental health literacy | 29 | 109.30 | 11.31 | 0.78 | 0.83 | – | |||
| 2. Knowledge of mental health problems | 11 | 41.18 | 5.28 | 0.72 | 0.81 | 0.81b | – | ||
| 3. Erroneous beliefs and stereotypes | 8 | 29.82 | 4.78 | 0.67 | 0.78 | 0.69b | 0.43b | – | |
| 4. First-aid skills and help-seeking behavior | 6 | 22.44 | 4.39 | 0.70 | 0.81 | 0.55b | 0.25b | 0.05 | – |
| 5. Self-help strategies | 4 | 16.66 | 2.37 | 0.68 | 0.80 | 0.69b | 0.56b | 0.35b | 0.26b |
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aComposite reliability. bp<0.01.
Mental health literacy and demographic determinants
The relationship between demographic variables (age, gender, educational level), parental characteristics (education, occupation, economic status), and living situation with mental health literacy (MHL) is evaluated in Table 3. First, simple regression assessed each variable’s relationship with MHL. Then, multiple regression included significant variables (p<0.05) to test adjusted relationships.
Regression results: The association among mental health literacy and socio-demographic variables.
| MHL | Simple analysis | Adjusted analysis | ||||
|---|---|---|---|---|---|---|
| Variables | Mean | SD | β (SE) | p-Value | β (SE) | p-Value |
| Gender | ||||||
| Male | 107.56 | 11.15 | −3.88 (0.94) | <0.001 | −3.51 (0.96) | <0.001 |
| Female | 111.44 | 11.16 | Ref | Ref | ||
| Grade | 0.59b | 0.33 (0.58) | 0.57b | |||
| 1 | 109.86 | 11.19 | −0.62 (1.17) | 0.60 | ||
| 2 | 107.95 | 10.97 | −2.53 (1.15) | 0.03 | ||
| 3 | 110.48 | 11.68 | Ref | |||
| Mother’s education | 0.03b | 0.50 (0.24) | 0.04b | |||
| Uneducated | 106.83 | 12,83 | −5.64 (2.63) | 0.03 | ||
| Primary | 109.32 | 10.10 | −3.15 (1.61) | 0.05 | ||
| High School | 108.00 | 10.96 | −4.47 (1.59) | 0.01 | ||
| Diploma | 109.20 | 11.18 | −3.26 (1.40) | 0.02 | ||
| University | 112.47 | 12.62 | Ref | |||
| Father’s education | 0.04b | 0.36 (0.31) | 0.24b | |||
| Uneducated | 107.14 | 14.67 | −3.09 (4.40) | 0.48 | ||
| Primary | 108.16 | 10.83 | −2.07 (1.50) | 0.17 | ||
| high School | 108.78 | 11.19 | −1.45 (1.42) | 0.31 | ||
| Diploma | 110.09 | 11.54 | −0.14 (1.31) | 0.92 | ||
| University | 110.23 | 11.33 | Ref | |||
| Mother’s occupation | ||||||
| Household | 109.08 | 11.34 | −3.86 (1.10) | 0.02 | −2.33 (1.01) | 0.02 |
| Employed | 112.94 | 10.47 | Ref | Ref | ||
| Father’s occupation | ||||||
| Unemployed | 108.75 | 11.25 | −2.83 (1.12) | 0.01 | −1.92 (0.91) | 0.03 |
| Employed | 111.58 | 11.29 | Ref | Ref | ||
| Economic status | 0.74b | |||||
| Weak | 106.79 | 11.17 | −2.63 (1.90) | 0.17 | ||
| Middle | 109.75 | 11.45 | 0.33 (1.02) | 0.74 | ||
| Good | 109.42 | 11.10 | Ref | |||
| Live with | ||||||
| Parents | 109.50 | 11.31 | 1.59 (2.03) | 0.43 | ||
| Other | 107.91 | 11.35 | Ref | |||
| Age | −0.12 (0.51) | 0.81b | ||||
| Mother’s age | 0.13 (0.08) | 0.13b | ||||
| Father’s age | 0.13 (0.07) | 0.10b | ||||
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aLiving with parents refers to living with both father and mother in the same household. Living with others refers to living with only the father, only the mother, or with other guardians. bP for trend
In simple analysis, male students had significantly lower MHL than females (β=−3.88, p<0.001), remaining significant after adjustment (β=−3.51, p<0.001). Grade 2 students showed lower MHL than grade 3 (β=−2.53, p=0.03), while grade 1 showed no difference (β=−0.62, p=0.60). This association became non-significant after adjustment (p=0.57).
For parental education, higher maternal education predicted higher MHL. Students with uneducated mothers had lower MHL than those with university-educated mothers (β=−5.64, p=0.03), remaining significant after adjustment (p=0.04). No significant associations appeared for father’s education (p=0.24 after adjustment).
Regarding occupation, students with employed mothers had higher MHL than those with homemaker mothers (β=−3.86, p=0.02; adjusted β=−2.33, p=0.02). Similarly, students with employed fathers showed higher MHL than those with free worker fathers (β=−2.83, p=0.01; adjusted β=−1.92, p=0.03). No significant associations appeared for economic status, living situation, or ages (p>0.05).
The results indicate gender, mother’s education, and parental occupation significantly predict MHL. Male students, those with less-educated mothers, and those with homemaker/free worker parents had lower MHL scores, with associations remaining significant after adjustment.
The MANOVA results in Table 4 show significant associations between socio-demographic variables and mental health literacy subscales. Gender differences were notable, with male students scoring lower in Knowledge of Mental Health Problems (β=−1.83, p<0.001) and Erroneous Beliefs and Stereotypes (β=−2.40, p<0.001) than females. Males scored higher in Help-Seeking Behavior (β=0.82, p=0.03), while females had higher Self-Help Strategies scores (β=0.54, p=0.01), indicating gender’s crucial role.
MANOVA results: The association among subscales of mental health literacy and socio-demographic variables.
| Knowledge of mental health problems | Erroneous beliefs and stereotypes | Help-seeking behavior | Self-help strategies | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Categorical variables | Mean | SD | p-Value | Mean | SD | p-Value | Mean | SD | p-Value | Mean | SD | p-Value |
| Gender | <0.001 | <0.001 | 0.03 | 0.01 | ||||||||
| Male | 40.36 | 5.05 | 28.69 | 4.62 | 22.86 | 4.22 | 16.43 | 2.37 | ||||
| Female | 42.19 | 5.06 | 31.09 | 4.66 | 22.04 | 4.42 | 16.97 | 2.21 | ||||
| Grade | 0.16 | 0.11 | 0.18 | 0.46 | ||||||||
| 1 | 41.07 | 4.98 | 30.07 | 4.91 | 22.64 | 4.46 | 16.70 | 2.38 | ||||
| 2 | 40.83 | 5.42 | 29.25 | 4.67 | 22.01 | 4.21 | 16.82 | 2.25 | ||||
| 3 | 41.80 | 4.94 | 30.20 | 4.75 | 22.78 | 4.32 | 16.53 | 2.30 | ||||
| Mother’s education | 0.003 | 0.04 | 0.95 | 0.40 | ||||||||
| Uneducated | 39.4 | 6.1 | 29.0 | 5.0 | 23.1 | 4.2 | 16.3 | 2.3 | ||||
| Primary | 40.7 | 4.6 | 30.1 | 4.1 | 22.6 | 4.0 | 16.7 | 2.0 | ||||
| High School | 40.6 | 5.2 | 29.0 | 4.7 | 22.3 | 4.6 | 16.9 | 2.0 | ||||
| Diploma | 41.3 | 5.2 | 29.7 | 4.9 | 22.4 | 4.4 | 16.5 | 2.4 | ||||
| University | 42.9 | 4.9 | 30.9 | 5.0 | 22.5 | 4.3 | 17.0 | 2.6 | ||||
| Father’s education | 0.01 | 0.33 | 0.04 | 0.66 | ||||||||
| Uneducated | 39.71 | 7.20 | 29.71 | 4.11 | 22.86 | 3.93 | 16.00 | 2.89 | ||||
| Primary | 40.42 | 5.23 | 29.18 | 4.48 | 23.02 | 4.13 | 16.49 | 2.15 | ||||
| High School | 40.41 | 4.94 | 29.67 | 4.62 | 22.51 | 4.31 | 16.86 | 2.04 | ||||
| Diploma | 41.62 | 4.97 | 29.88 | 5.04 | 22.71 | 4.26 | 16.74 | 2.44 | ||||
| University | 42.32 | 5.18 | 30.51 | 4.80 | 21.54 | 4.60 | 16.62 | 2.49 | ||||
| Mother’s occupation | <0.001 | 0.04 | 0.49 | 0.03 | ||||||||
| Household | 41.01 | 5.15 | 29.72 | 4.80 | 22.51 | 4.30 | 16.62 | 2.32 | ||||
| Employed | 43.56 | 4.30 | 31.02 | 4.52 | 22.06 | 4.71 | 17.40 | 2.15 | ||||
| Father’s occupation | <0.001 | 0.035 | 0.15 | 0.16 | ||||||||
| Unemployed | 40.70 | 5.11 | 29.59 | 4.70 | 22.62 | 4.29 | 16.61 | 2.35 | ||||
| Employed | 42.97 | 4.83 | 30.60 | 5.00 | 22.00 | 4.46 | 16.94 | 2.18 | ||||
| Economic status | 0.38 | 0.19 | 015 | 0.29 | ||||||||
| Weak | 40.35 | 4.72 | 29.53 | 3.75 | 21.60 | 4.53 | 16.19 | 2.57 | ||||
| Middle | 41.17 | 5.14 | 30.15 | 4.79 | 22.34 | 4.39 | 16.77 | 2.27 | ||||
| Good | 41.51 | 5.20 | 29.39 | 4.95 | 22.87 | 4.19 | 16.66 | 2.31 | ||||
| Live with | 0.71 | 0.70 | 0.57 | 0.30 | ||||||||
| Parents | 41.25 | 5.15 | 29.85 | 4.82 | 22.50 | 4.31 | 16.71 | 2.29 | ||||
| Other | 40.91 | 4.95 | 29.52 | 4.25 | 22.06 | 4.68 | 16.27 | 2.60 | ||||
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|
||||||||||||
| Continuous variables | Β | SE | p-Value | β | SE | p-Value | β | SE | p-Value | β | SE | p-Value |
|
|
||||||||||||
| Age | 0.17 | 0.23 | 0.46 | 0.10 | 0.21 | 0.44 | −0.18 | 0.19 | 0.35 | −0.12 | 0.10 | 0.27 |
| Mother’s age | 0.06 | 0.04 | 0.14 | 0.04 | 0.03 | 0.25 | 0.02 | 0.03 | 0.60 | 0.03 | 0.02 | 0.14 |
| Father’s age | 0.34 | 0.33 | 0.31 | 0.20 | 0.31 | 0.52 | −0.16 | 0.28 | 0.58 | 0.01 | 0.15 | 0.97 |
Mother’s education level significantly associated with Knowledge of Mental Health Problems (p=0.003) and Erroneous Beliefs and Stereotypes (p=0.04). Students with university-educated mothers scored higher in Knowledge (Mean=42.9, SD=4.9) than those with uneducated mothers (Mean=39.4, SD=6.1). Father’s education associated with Knowledge (p=0.01) and Help-Seeking Behavior (p=0.04). While Erroneous Beliefs scores didn’t differ significantly by father’s education, they increased with higher education levels (β=0.39, SE=0.19; p=0.04).
Students with employed mothers had higher Knowledge (Mean=43.56, SD=4.30, p<0.001) and Erroneous Beliefs scores (Mean=31.02, SD=4.52, p=0.04) than those with homemaker mothers (Mean=41.01, SD=5.15; Mean=29.72, SD=4.80). Similarly, students with employed fathers had higher Knowledge (Mean=42.97, SD=4.83, p<0.001) and Erroneous Beliefs scores (Mean=30.60, SD=5.00, p=0.035) than those with free worker fathers (Mean=40.70, SD=5.11; Mean=29.59, SD=4.70).
Economic status showed no significant associations except for Help-Seeking Behavior, which increased with higher economic levels (β=0.39, SE=0.29; p=0.04). Other variables (grade, living situation, age) showed no significant associations, suggesting gender, parental education, and occupation are key influences on mental health literacy.
Discussion
The present study revealed that mental health literacy (MHL) was significantly higher among female students compared to male students. Farhadpoor et al., in their study on students, found that the average MHL score was higher among women than men, with a significant difference between genders, indicating that female students had higher MHL than male students [27]. Gibbons et al. reported that men demonstrated weaker mental health literacy skills compared to women in their study. Men were less likely to correctly identify mental illnesses, often did not take symptoms seriously, and were less likely to acknowledge the need for treatment [28]. Additionally, in the dimensions of MHL, girls scored higher than boys in three areas: Knowledge of Mental Health Problems, Erroneous Beliefs and Stereotypes, and Self-Help Strategies. Gibbons et al. noted that men showed poorer awareness of mental health issues compared to women, making it more likely for mental illnesses to be correctly identified in women, who also perceived symptoms more seriously and confirmed the need for treatment [29]. Petersen’s review indicated that help-seeking strategies and erroneous beliefs in MHL were lower among boys than girls. This research suggests that educational interventions in this area can enhance MHL among boys [30]. The higher MHL and its dimensions among girls can be attributed to various factors. Girls generally pay more attention to their emotional and psychological well-being, leading to greater awareness of mental health issues and their symptoms. Additionally, better access to educational and supportive resources, such as workshops and counseling programs, contributes to increase MHL among girls. In contrast, boys, due to social and cultural pressures, may be less inclined to express their mental health issues, which can result in lower MHL. These differences highlight the need for gender-specific educational programs to improve students’ mental health and reduce existing barriers. Furthermore, promoting early intervention and prevention of mental health issues among adolescents, including an educational program that covers the mechanisms of mental illness, symptoms, self-help strategies, and reduction of social stigma, can be effective in enhancing youth health literacy [31].
Parental education particularly that of mothers, plays a significant role in shaping children’s MHL. Mashayekhi et al. found that maternal education significantly influences MHL, and educated parents are more likely to provide better advice on mental health issues to their children [32]. Bignold also emphasized that parental MHL is crucial for supporting adolescents and influences their intention to seek mental health services [33]. Higher education among women can significantly increase awareness of mental health issues, reduce erroneous beliefs, and enhance students’ MHL [22]. These findings align with the results of the current study. Women with higher education typically have greater access to mental health resources and information, enabling them to better identify and manage mental health issues for themselves and others. This awareness and knowledge allow them to serve as role models for students, promoting the importance of mental health in daily life. Furthermore, educated women often hold better social and professional positions, enabling them to provide more educational and supportive programs for students [34]. These programs may include workshops, counseling sessions, and group activities that increase awareness and reduce stress and anxiety among students. Consequently, higher education among women not only improves their own mental health but also positively impacts the MHL of future generations.
Another significant finding of this study is the notable influence of parental employment on children’s MHL. Yap et al. demonstrated in their research that parents who received mental health information at work were more likely to correctly affirm the belief that showing affection to their children could prevent depression, indicating a positive relationship between employed parents and MHL [35]. According to Solhi et al., paternal employment is associated with mental health awareness and self-help strategies, and parental employment enhances children’s MHL [11]. The link between adolescents’ MHL and their parents’ employment may be related to the better financial status of employed parents, as many mental health services require co-payments, and better financial resources can help adolescents access these services [36].The relationship between parental employment and children’s mental health literacy (MHL) is influenced by various factors. Employed parents serve as behavioral role models, imparting the significance of discipline to their children. Additionally, their experiences in the workplace provide them with a deeper understanding of the challenges of everyday life, which they can convey to their children. Furthermore, access to educational resources and robust social networks enables parents to acquire essential knowledge regarding mental health, which they can subsequently pass on to their children [37].
Among the limitations of the present study is the reliance on self-reported data from students, which may introduce biases, such as social desirability bias, whereby participants may provide responses they believe are more acceptable rather than reflecting their true feelings or knowledge. Additionally, as a cross-sectional study, data were collected at a specific point in time, which limits the ability to establish causal relationships between factors and mental health literacy. Furthermore, this study may not account for all contextual factors influencing mental health literacy, such as family dynamics, socioeconomic status, or cultural influences, which could provide a more comprehensive understanding of the contributing factors.
Conclusion
Based on these findings, it is essential to design and implement targeted educational and supportive programs aimed at enhancing mental health literacy (MHL) among male students. These initiatives should specifically address the unique needs and challenges faced by this demographic. Additionally, providing mental health-related training in workplace settings can significantly improve parents’ MHL, thereby facilitating the effective transfer of this critical knowledge to their children. Furthermore, it is recommended that educational programs incorporate practical workshops and group interactions to promote more active learning. Finally, emphasizing the pivotal role of educated mothers in fostering mental health awareness is crucial. By providing appropriate resources and programs, we can enhance mental health literacy (MHL) not only in the present but also ensure its transmission to future generations. This approach may lead to increased awareness among youth and parents regarding mental health issues, encouraging them to seek help when needed.
Acknowledgments
We are thankful of all schools authorities and students who contributed in this study. The authors acknowledge the use of ChatGPT (OpenAI) for language editing and translation. The authors carefully verified the accuracy of the content and take full responsibility for of the text.
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Research ethics: This study was approved by the Ethics Committee for Research at Khoy University of Medical Sciences under ethics code IR.KHOY.REC.1402.020.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: Zeinab Habibpour developed the original idea and gathered data, MoradAli Zareipour contributed to the development of the prepared manuscript. Mohammad Saadati and Roghieh Sodeify wrote the manuscript, and Mehrdad Karimi analyzed the data.
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Use of Large Language Models, AI and Machine Learning Tools: The authors acknowledge the use of ChatGPT (OpenAI) for language editing and translation.
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Conflict of interest: Authors state no conflict of interest.
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Research funding: This research is financially supported by khoy University of Medical Sciences.
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Data availability: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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