psych Psychology 2152-7180 2152-7199 Scientific Research Publishing 10.4236/psych.2025.163020 psych-141636 Articles Social Sciences Humanities Mental Health Literacy among Middle School Students in Private and Community Schools, Kathmandu, Nepal Ashish Shrestha 1 Dev Bandhu Poudel 1 2 3 4 Purusottam Thapa 1 aSambhavya Foundation, Lalitpur, Nepal aG. P. Koirala Memorial (Community) College, Kathmandu, Nepal aBrookyn College, Kathmandu, Nepal aRupy’s International School (A-Level)—Cambridge Associate School, Kathmandu, Nepal 11 03 2025 16 03 333 352 7, February 2025 24, February 2025 24, March 2025 © Copyright 2014 by authors and Scientific Research Publishing Inc. 2014 This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Mental health literacy (MHL) among adolescents is crucial for early recognition and intervention of mental health issues, yet research on MHL among Nepalese adolescents remains limited. This study aimed to assess mental health literacy and its relationship with demographic variables among school-going adolescents in Nepal. Methods: A cross-sectional survey using the Mental Health Literacy Questionnaire (MHLQ) was conducted among 454 students (56.82% male, 43.18% female) aged 12 – 16-year from seven schools in Kathmandu and Lalitpur districts, selected through purposive sampling. Data normality was assessed with the Shapiro-Wilk test. Descriptive and inferential statistics were applied, with effect sizes calculated using Hedges’ g, partial eta squared and Cohen’s d. Reliability was measured with McDonald’s ω and Guttman’s λ6. Spearman’s rho (ρ) was used for correlation analysis. Results: The study revealed significantly higher levels of global mental health literacy and knowledge about mental health problems among females compared to males. Parental education significantly influenced MHL, with students whose parents held bachelor’s or master’s degrees demonstrating better literacy compared to those with primary-level educated parents. Females with higher parental education had greater mental health literacy, while among males, only those with bachelor’s-educated parents showed higher literacy. While participants showed high awareness of common mental health conditions like depression (84.36%) and anxiety (52.86%), recognition of less common conditions was notably low. No significant differences were observed across ethnicity, permanent residence, or grade levels. Conclusion: The findings underscore the need for targeted interventions to enhance mental health literacy, particularly among male students and those from families with lower educational backgrounds, while emphasizing comprehensive mental health education covering both common and less common mental health conditions. Adolescents Gender Differences Mental Health Literacy Nepal Parental Education School Type 1. Introduction

Mental Health Literacy (MHL) refers to the knowledge and beliefs about mental health disorders that facilitate their recognition, management, and prevention ( Jorm et al., 1997 ). Since its introduction, MHL has evolved into a multidimensional construct encompassing several essential components: the ability to recognize specific disorders, understanding the risk factors and causes, knowledge of self-treatment options, awareness of professional help available, and attitudes that encourage appropriate help-seeking ( Jorm, 2000 ; O’Connor & Casey, 2015 ).

Mental health literacy (MHL) stems from the broader concept of health literacy (HL) ( Kutcher et al., 2016a ). Over time, the concept of MHL has shifted from a narrow focus on mental illness to being seen as a resource that can be enhanced through educational initiatives, aimed at improving public health ( Kutcher et al., 2016b ). Mental health literacy has been the most frequently examined topic, with studies primarily conducted in school-based settings and high-income economies ( Patafio et al., 2021 ). However, the concept of mental health literacy (MHL) is still relatively new and underexplored in school settings in countries like Nepal.

While mental health literacy (MHL) has been widely studied globally, there is a notable lack of research on MHL in Nepal, particularly among children and adolescents, including middle school students. We found only two articles closely related to our area of interest; however, both of the studies have been conducted in adults. One study was in college students and the other in community people ( Poudel et al., 2024 ; Singh et al., 2013 ).

Recent studies on mental health literacy (MHL) in adults have yielded mixed findings. Poudel et al. (2024) found no connection between age and MHL, contrasting with research suggesting lower knowledge among individuals aged 70 and above and higher MHL in younger adults aged 18 to 29 ( Doumit et al., 2019 ; Hadjimina & Furnham, 2017 ).

Additionally, Poudel et al. (2024) identified gender differences in erroneous beliefs or stereotypes but reported no significant variations in other factors such as ethnicity and academic level. However, their study does not address MHL in school-age students, leaving a critical gap in understanding MHL at earlier developmental stages.

Other factors influencing MHL, such as residency and educational context, also warrant further exploration. For instance, Singh et al. (2013) found that adults residing in urban areas exhibited better knowledge of mental health and illness compared to their rural counterparts. However, little is known about how these disparities manifest in younger populations. Furthermore, variables such as school type (private vs. government), parental education, and students’ ability to recognize of mental health problems have not been thoroughly examined for their impact on MHL in children and adolescents.

Despite global research emphasizing the importance of MHL, studies in Nepal have predominantly focused on adults, leaving the needs of children and adolescents, particularly middle school students, underexplored. Limited attention has been given to how demographic factors like age, gender, ethnicity, grades, school type, parental education and the combined role of gender and parental education influence MHL in this population.

This study aims to address these gaps by investigating MHL among middle school students in Nepal. By examining disparities and factors such as age, gender, ethnicity, residence (valley vs outside valley), grades, school type, parental education and the interplay of gender and parental education this research seeks to provide insights that inform targeted interventions and promote better mental health outcomes for this underserved group.

This study is significant as it addresses the crucial role of MHL in promoting early intervention, reducing stigma, and fostering a supportive community. By investigating MHL among middle school students, it aims to provide insights that can guide targeted educational programs and policies, improving mental health outcomes and academic performance, ultimately benefiting both individuals and society.

This study is limited to select schools in Kathmandu, affecting the generalizability of findings to other regions of Nepal. It focuses on schools with prior access to mental health services and excludes primary grade students, narrowing the scope of MHL assessment. Data collection through interviews, observations, and the Mental Health Literacy Questionnaire (MHLQ) may introduce response bias. While the sample size is 454, the study does not evaluate the effectiveness of the examined mental health services and is restricted to comparisons between private and community schools, limiting broader applicability.

 2. Methodology 2.1. Research Design

In this study, we employed a cross-sectional survey design. Quantitative data were collected using a purposive sampling technique from both community and private schools based in Kathmandu districts. Purposive sampling was used to ensure diverse representation across key demographics (e.g., gender, ethnicity, school type, and parental education) in Kathmandu and Lalitpur districts. This approach allowed for a balanced sample from both community and private schools, targeting adolescents aged 12 - 16 and in grades 7 - 9, reflecting the region’s demographic diversity.

 2.2. Ethical Considerations

Prior to data collection, approvals were obtained from the respective school authorities, and informed consent was secured from all participants. Ethical guidelines were strictly followed, particularly because the participants were minors. Measures were implemented to ensure privacy and confidentiality, participant protection, the right to withdraw, and data security, including the use of a password-protected database. Ethical data analysis was conducted using appropriate statistical techniques with open-source software (Jeffreys’s Amazing Statistics Program, JASP).

 2.3. Participants

A total of 454 participants were recruited from seven schools, comprising three private and four community institutions in the Lalitpur and Kathmandu districts. The study involved students aged 12 to 16 years, with 11.70% aged 12 or below, 22.52% aged 13, 27.37% aged 14, 21.19% aged 15, and 17.21% aged 16 or above. The gender distribution included 56.82% male and 43.18% female students. Ethnic representation consisted of 20.17% Janajati, 57.46% Khas/Aryan, and 22.38% Newar. Parental education levels varied, with 25.30% completing the primary level (PL), 15.51% achieving the Secondary Education Examination (SEE), 14.80% completing a high school degree (HSD), 14.80% holding a Bachelor’s degree (BD), and 29.59% attaining a Master’s degree (MD). Grade levels included 14.75% in Grade 7, 22.46% in Grade 8, and 62.77% in Grade 9.

 2.4. Materials

The 33-item Mental Health Literacy Questionnaire (MHLQ; Campos et al., 2016 ) was used to assess knowledge, beliefs, and attitudes. The instrument includes 33 statement-based questions and one multiple-choice item. The scale demonstrated strong reliability (α = 0.84) overall, as well as for its factors: Help-Seeking and First Aid Skills (HSFAS) (α = 0.79), Knowledge/Stereotypes about Mental Health Problems (KSMHP) (α = 0.78), and Self-Help Strategies (SHS) (α = 0.72). It also exhibited excellent test-retest reliability, with an ICC of 0.88 for the total MHLq score and 0.80, 0.90, and 0.86 for Factors 1, 2, and 3, respectively. Items are rated on a five-point scale (1 = strongly disagree to 5 = strongly agree), with specific ones reverse-scored ( Campos et al., 2016 ; Dias et al., 2018 ). In this current study, we observed acceptable reliability score, McDonald’s ω  =  0.78, 95% CI [0.75, 0.81] and Guttman’s λ6 = 0.83, 95% CI [0.81, 0.87]. The MHLQ total scale showed moderate to strong positive correlations measured by Spearman’s rho (ρ) with its factors: HSFAS (ρ = 0.66, p < 0.001), KSMHP (ρ = 0.81, p < 0.001), and SHS (ρ = 0.57, p < 0.001), demonstrating a strong convergent validity.

 2.5. Procedures

The study employed purposive sampling to select participants, focusing on students from private and public schools in the Kathmandu and Lalitpur districts. This targeted approach aimed to capture a diverse representation of the population in terms of age, gender, ethnicity, parental education, and school type, grade levels, ensuring comprehensive data reflective of the region’s demographic variety.

A brief orientation regarding the study’s objectives and data collection methods was explained to all the participants. During this session, student inquiries were addressed on an individual basis, ensuring clarity and understanding. The questionnaire began with written informed consent form, demographic information form, followed by the MHLQ.

Class teachers were engaged to facilitate the distribution of the questionnaires, ensuring a supportive environment for data collection. Following the data collection, all completed surveys were reviewed for accuracy and completeness, identifying any missing or illegible responses.

To minimize measurement bias, reliable and validated tools were used, with translations into Nepali following established protocols. The tools’ reliability and validity were assessed in the Nepalese context as well. To address response bias in the on-school survey design, comprehensive information about the research was provided to participants, ensuring confidentiality. Convenient sampling was preferred over purposive sampling for systematic investigation, emphasizing careful observation and understanding of survey items, with clarifications offered for any ambiguities.

 2.6. Data Analysis and Reporting

Quantitative data from the MHLQ were analyzed to assess variations among demographic factors using both descriptive and inferential statistics, including means and standard deviations. To evaluate relationships between variables, Welch’s t-test and ANOVA (with Welch’s correction for homogeneity) were conducted based on the results of parametric tests (Shapiro-Wilk test). Games-Howell post-hoc comparisons were applied for significant group differences. For effect size, Hedges’ g was calculated for the t-test, partial eta squared (ηp²) for ANOVA, and Cohen’s d for post hoc comparisons. Spearman’s rho (ρ) was used to measure correlations among variables, and reliability was assessed using McDonald’s omega (ω) and Guttman’s λ6. Note: Please report Reliability first as in other areas.

Data were cleaned via Google sheet and downloaded as a CSV file for analysis in Jeffrey’s Amazing Statistics Program (JASP). Data were visually represented in tables and figures. Citations were managed using Mendeley, and the report was prepared in Microsoft Word. ChatGPT was used for paraphrasing and language editing.

 3. Results 3.1. Descriptive and Demographic Component

The total score ranged from 57 to 154 (M = 126.30, SD = 13.27) in the MHLQ. The 25th percentile was 121, the 50th percentile (median) was 128, and the 75th percentile was 135. Skewness was −1.19 (SE = 0.15), and kurtosis was 3.13 (SE = 0.30). The Shapiro-Wilk test indicated significant deviations from a normal distribution for the MHLQ total score (W = 0.94, p < 0.001) and across various demographic subgroups: gender (W = 0.94, p < 0.001), ethnicity (W = 0.96, p = 0.006), grades (W = 0.98, p < 0.001), location (W = 0.94, p < 0.001), and types of school (W = 0.92, p < 0.001) ( Table 1 ).

<xref ref-type="bibr" rid="scirp.141636-"></xref>Table 1. Descriptive statistics.

Variables

N (%)

Skewness

Kurtosis

Shapiro-Wilk

p-value of Shapiro-Wilk

Gender

HSFAS Factor

Female

196 (43.17%)

−0.48

0.22

0.98

0.004**

HSFAS Factor

Male

258 (56.83%)

−0.88

1.22

0.96

<0.001***

KSMHP Factor

Female

196 (43.17%)

−0.33

−0.51

0.98

0.009**

KSMHP Factor

Male

258 (56.83%)

−0.70

0.96

0.97

<0.001***

SHS Factor

Female

196 (43.17%)

−0.44

0.05

0.97

<0.001***

SHS Factor

Male

258 (56.83%)

−1.00

2.31

0.94

<0.001***

MHLQ Total

Female

196 (43.17%)

−0.41

−0.31

0.98

0.011*

MHLQ Total

Male

258 (56.83%)

−1.19

3.13

0.94

<0.001***

Ethnicity

HSFAS Factor

Janajaati

73 (16.08%)

−0.48

−0.26

0.96

0.035*

HSFAS Factor

Khas/Aryan

208 (45.82%)

−0.88

1.71

0.96

<0.001***

HSFAS Factor

Newar

81 (17.84%)

−0.69

0.80

0.96

0.021*

KSMHP Factor

Janajaati

73 (16.08%)

−0.20

−0.25

0.99

0.609

KSMHP Factor

Khas/Aryan

208 (45.82%)

−0.73

0.79

0.97

<0.001***

KSMHP Factor

Newar

81 (17.84%)

−1.11

3.33

0.93

<0.001***

SHS Factor

Janajaati

73 (16.08%)

−0.34

−0.44

0.96

0.015*

SHS Factor

Khas/Aryan

208 (45.82%)

−0.96

2.01

0.95

<0.001***

SHS Factor

Newar

81 (17.84%)

−0.39

0.72

0.97

0.042*

MHLQ Total

Janajaati

73 (16.08%)

−0.09

−0.62

0.99

0.634

MHLQ Total

Khas/Aryan

208 (45.82%)

−1.45

4.41

0.91

<0.001*

MHLQ Total

Newar

81 (17.84%)

−0.91

1.64

0.96

0.006**

Permanent Residence

HSFAS Factor

Inside Valley

185 (40.75%)

−0.64

0.57

0.97

<0.001***

HSFAS Factor

Outside Valley

236 (51.98%)

−0.98

2.00

0.95

<0.001***

KSMHP Factor

Inside Valley

185 (40.75%)

−0.81

1.53

0.96

<0.001***

KSMHP Factor

Outside Valley

236 (51.98%)

−0.49

0.34

0.98

0.002**

SHS Factor

Inside Valley

185 (40.75%)

−0.49

0.31

0.97

<0.001***

SHS Factor

Outside Valley

236 (51.98%)

−0.86

1.83

0.95

<0.001***

MHLQ Total

Inside Valley

185 (40.75%)

−0.96

1.41

0.95

<0.001***

MHLQ Total

Outside Valley

236 (51.98%)

−1.15

4.02

0.94

<0.001***

Continued

Grades

HSFAS Factor

Seven

67 (14.76%)

−1.07

2.64

0.94

0.002

HSFAS Factor

Eight

102 (22.47%)

−0.32

0.16

0.98

0.227

HSFAS Factor

Nine

285 (62.78%)

−0.81

0.71

0.96

<0.001***

KSMHP Factor

Seven

67 (14.76%)

−1.12

1.31

0.92

<0.001***

KSMHP Factor

Eight

102 (22.47%)

−0.52

0.04

0.97

0.03*

KSMHP Factor

Nine

285 (62.78%)

−0.28

−0.19

0.99

0.03*

SHS Factor

Seven

67 (14.76%)

−1.20

3.35

0.92

<0.001***

SHS Factor

Eight

102 (22.47%)

−0.77

1.13

0.95

<0.001***

SHS Factor

Nine

285 (62.78%)

−0.55

0.29

0.97

<0.001***

MHLQ Total

Seven

67 (14.76%)

−1.60

4.43

0.89

<0.001***

MHLQ Total

Eight

102 (22.47%)

−0.89

0.93

0.95

<0.001***

MHLQ Total

Nine

285 (62.78%)

−0.46

0.00

0.98

<0.001***

Types of Institutions

HSFAS Factor

Government School

198 (43.61%)

−0.83

0.74

0.95

<0.001***

HSFAS Factor

Private School

256 (56.39%)

−0.80

1.60

0.97

<0.001***

KSMHP Factor

Government School

198 (43.61%)

−0.22

−0.28

0.99

0.066

KSMHP Factor

Private School

256 (56.39%)

−1.02

1.90

0.94

<0.001***

SHS Factor

Government School

198 (43.61%)

−0.64

0.83

0.96

<0.001***

SHS Factor

Private School

256 (56.39%)

−0.81

1.47

0.95

<0.001***

MHLQ Total

Government School

198 (43.61%)

−0.54

0.38

0.98

0.002**

MHLQ Total

Private School

256 (56.39%)

−1.34

3.95

0.92

<0.001***

Parental Education

HSFAS Factor

Primary Level (Class 1 - 8)

106 (23.35%)

−0.44

0.09

0.98

0.096

HSFAS Factor

Junior HSD(Class 9 & 10)

65 (14.32%)

−0.80

0.41

0.95

0.006**

HSFAS Factor

Senior HSD(Class 11 & 12)

62 (13.66%)

−0.88

0.66

0.93

0.002**

HSFAS Factor

Bachelor’s Degree

63 (13.66%)

−0.77

0.87

0.95

0.021*

HSFAS Factor

Master’s Degree

124 (27.31%)

−0.79

1.06

0.96

<0.001***

KSMHP Factor

Primary Level(Class 1 - 8)

106 (23.35%)

−0.50

1.15

0.98

0.06

KSMHP Factor

Junior HSD(Class 9 & 10)

65 (14.32%)

−0.15

−0.40

0.99

0.796

KSMHP Factor

Senior HSD(Class 11 & 12)

62 (13.66%)

−0.71

0.70

0.96

0.04*

KSMHP Factor

Bachelor’s Degree

63 (13.66%)

−0.46

0.06

0.96

0.033*

KSMHP Factor

Master’s Degree

124 (27.31%)

−0.55

0.12

0.97

0.004**

SHS Factor

Primary Level(Class 1 - 8)

106 (23.35%)

−0.22

−0.77

0.96

0.004**

Continued

SHS Factor

Junior HSD(Class 9 & 10)

65 (14.32%)

−0.83

1.13

0.94

0.005**

SHS Factor

Senior HSD(Class 11 & 12)

62 (13.66%)

−0.95

1.66

0.94

0.004**

SHS Factor

Bachelor’s Degree

63 (13.66%)

−0.61

0.34

0.97

0.072

SHS Factor

Master’s Degree

124 (27.31%)

−0.59

0.47

0.96

0.002**

MHLQ Total

Primary Level(Class 1 - 8)

106 (23.35%)

−0.16

−0.53

0.99

0.558

MHLQ Total

Junior HSD(Class 9 & 10)

65 (14.32%)

−0.28

−0.35

0.98

0.402

MHLQ Total

Senior HSD(Class 11 & 12)

62 (13.66%)

−0.91

1.68

0.96

0.023*

MHLQ Total

Bachelor’s Degree

63 (13.66%)

−0.79

1.07

0.95

0.018*

MHLQ Total

Master’s Degree

124 (27.31%)

−1.00

1.49

0.94

<0.001***

Gender and Parental Education Combined Interaction

HSFAS Factor

Female (Bachelor+)

68 (16.22%)

−0.26

−0.27

0.97

0.143

HSFAS Factor

Female (Senior HSD)

30 (7.16%)

−0.97

0.84

0.92

0.027*

HSFAS Factor

Female (Junior HSD)

33 (7.88%)

−0.21

−0.83

0.96

0.321

HSFAS Factor

Female (Primary)

54 (12.89%)

−0.31

−0.47

0.97

0.215

HSFAS Factor

Male (Bachelor+)

118 (28.16%)

−0.95

1.17

0.94

<0.001***

HSFAS Factor

Male (Senior HSD)

32 (7.88%)

−0.81

0.7

0.94

0.056

HSFAS Factor

Male (Junior HSD)

32 (7.64%)

−0.99

0.35

0.9

0.006**

HSFAS Factor

Male (Primary)

52 (12.41%)

−0.3

0.0003

0.98

0.557

KSMHP Factor

Female (Bachelor+)

68 (16.22%)

−0.71

−0.02

0.95

0.007**

KSMHP Factor

Female (Senior HSD)

30 (7.16%)

−0.33

−0.4

0.96

0.385

KSMHP Factor

Female (Junior HSD)

33 (7.88%)

−0.53

0.49

0.97

0.387

KSMHP Factor

Female (Primary)

54 (12.89%)

0.07

−0.58

0.98

0.457

KSMHP Factor

Male (Bachelor+)

118 (28.16%)

−0.48

0.34

0.97

0.004**

KSMHP Factor

Male (Senior HSD)

32 (7.88%)

−0.48

0.38

0.97

0.397

KSMHP Factor

Male (Junior HSD)

32 (7.64%)

0.1

−0.36

0.98

0.895

KSMHP Factor

Male (Primary)

52 (12.41%)

−0.81

1.7

0.96

0.059

SHS Factor

Female (Bachelor+)

68 (16.22%)

−0.45

0.31

0.97

0.095

SHS Factor

Female (Senior HSD)

30 (7.16%)

−0.46

−0.98

0.9

0.01**

SHS Factor

Female (Junior HSD)

33 (7.88%)

−0.72

0.63

0.95

0.128

SHS Factor

Female (Primary)

54 (12.89%)

−0.26

−0.98

0.94

0.009**

SHS Factor

Male (Bachelor+)

118 (28.16%)

−0.67

0.55

0.96

0.001**

SHS Factor

Male (Senior HSD)

32 (7.88%)

−0.87

1.13

0.94

0.072*

SHS Factor

Male (Junior HSD)

32 (7.64%)

−0.37

−0.14

0.97

0.398

SHS Factor

Male (Primary)

52 (12.41%)

−0.15

−0.56

0.96

0.103

MHLQ Total

Female (Bachelor+)

68 (16.22%)

−0.79

0.59

0.96

0.018*

Continued

MHLQ Total

Female (Senior HSD)

30 (7.16%)

−0.07

−0.58

0.98

0.884

MHLQ Total

Female (Junior HSD)

33 (7.88%)

−0.45

−0.08

0.97

0.351

MHLQ Total

Female (Primary)

54 (12.89%)

−0.1

−0.76

0.98

0.609

MHLQ Total

Male (Bachelor+)

118 (28.16%)

−0.98

1.58

0.94

<0.001

MHLQ Total

Male (Senior HSD)

32 (7.88%)

−0.94

1.24

0.94

0.089

MHLQ Total

Male (Junior HSD)

32 (7.64%)

−0.15

−0.28

0.99

0.931

MHLQ Total

Male (Primary)

52 (12.41%)

−0.38

−0.51

0.96

0.105

*p < 0.05, **p < 0.01, ***p < 0.001. Note: Skewness, Kurtosis, Shapiro-Wilk test values, and p-values are presented for each factor across different demographic groups.

The age ranged from 11 to 17 (M = 14.09, SD = 1.26). The sample consisted of 454 participants. Gender distribution was 56.82% male and 43.18% female ( Table 1 ).

 3.2. Relationship MHL with Demographic Variables

A significantly higher level was found in females compared to males in KSMHP dimension and the global MHL. However, no significant difference was found between gender variables in all other factors ( Table 2 ).

<xref ref-type="bibr" rid="scirp.141636-"></xref>Table 2. Welch’s t-test results for gender, permanent residence, and school type.

Group

Mean

SD

SE

t

df

p (Hedges’ g)

Gender

HSFAS Factor

Female

39.17

4.76

0.34

1.16

450.43

0.247

Male

38.59

5.90

0.37

KSMHP Factor

Female

70.74

7.21

0.52

4.31

443.80

<0.001***(g = 0.40)

Male

67.60

8.30

0.52

SHS Factor

Female

19.63

3.00

0.21

−1.66

426.97

0.097

Male

20.11

3.10

0.19

MHLq Total

Female

129.55

10.72

0.77

2.88

450.28

0.004**(g = 0.26)

Male

126.30

13.27

0.83

Permanent Residence

HSFAS Factor

Inside Valley

38.541

5.302

0.39

−1.39

402.52

0.167

Outside Valley

39.275

5.532

0.36

KSMHP Factor

Inside Valley

69.486

7.943

0.58

0.876

391.27

0.381

Outside Valley

68.809

7.772

0.51

SHS Factor

Inside Valley

19.919

2.83

0.21

−0.02

409.69

0.987

Outside Valley

19.924

3.108

0.2

MHLq Total

Inside Valley

127.95

11.57

0.85

−0.05

405.61

0.957

Outside Valley

128.01

12.32

0.8

Continued

Type of the Schools

HSFAS Factor

Government School

39.56

5.32

0.38

2.49

429.56

0.013*(g = 0.24)

Private School

38.29

5.48

0.34

KSMHP Factor

Government School

67.02

7.26

0.52

−4.71

444.15

<0.001***(g = 0.44)

Private School

70.45

8.22

0.51

SHS Factor

Government School

20.10

2.92

0.21

1.21

438.52

0.227

Private School

19.75

3.17

0.20

MHLq Total

Government School

126.68

11.37

0.81

−1.58

445.14

0.115

Private School

128.49

12.99

0.81

*p < 0.05, **p < 0.01, ***p < 0.001. Note: The table provides means, standard deviations (SD), standard errors (SE), t-values, degrees of freedom (df), p-values, and effect sizes (Hedges’ g) for comparisons across gender, permanent residence, and type of school. Effect sizes (g) are shown for significant results.

We found no statistically significant differences in the level of MHLq and its all dimension among ethnic groups i.e., Janajaati, Khas/Aryan, and Newar ( Table 3 ).

<xref ref-type="bibr" rid="scirp.141636-"></xref>

Table 3. Welch’s ANOVA for ethnicity, grades, and parental education.

Variables

Mean

SD

SE

df

F

p (ηp2)

Ethnicity

HSFAS Factor

Janajaati

39.08

5.07

0.59

2/359

0.77

0.464

Khas/Aryan

38.39

5.73

0.40

Newar

39.15

5.21

0.58

KSMHP Factor

Janajaati

68.78

6.79

0.79

2/359

0.32

0.73

Khas/Aryan

69.63

8.12

0.56

Newar

69.37

7.92

0.88

SHS Factor

Janajaati

20.30

3.06

0.36

2/359

0.81

0.446

Khas/Aryan

19.81

3.18

0.22

Newar

19.78

2.53

0.28

MHLq Total

Janajaati

128.16

10.75

1.26

2/359

0.05

0.948

Khas/Aryan

127.83

12.88

0.89

Newar

128.30

10.76

1.20

Grades

HSFAS Factor

Class Seven

37.72

6.25

0.76

2/451

1.73

0.179

Class Eight

38.92

5.03

0.50

Class Nine

39.08

5.36

0.32

KSMHP Factor

Class Seven

68.51

10.17

1.24

2/451

0.13

0.882

Class Eight

69.09

8.96

0.89

Class Nine

69.01

7.02

0.42

Continued

SHS Factor

Class Seven

19.78

3.52

0.43

2/451

0.50

0.608

Class Eight

19.69

3.15

0.31

Class Nine

20.01

2.92

0.17

MHLq Total

Class Seven

126.00

16.10

1.97

2/451

0.79

0.454

Class Eight

127.70

14.11

1.40

Class Nine

128.11

10.52

0.62

Parent's Education

HSFAS Factor

Primary Level (Class 1 - 8)

39.04

5.08

0.49

4/414

2.05

0.087

Junior HSD (Class 9 & 10)

40.34

4.86

0.60

Senior HSD (Class 11 & 12)

37.97

5.46

0.69

Bachelor’s Degree

38.16

5.81

0.74

Master’s Degree

39.14

5.28

0.48

KSMHP Factor

Primary Level (Class 1 - 8)

65.41

7.56

0.73

4/414

10.14

<0.001*** (0.089)

Junior HSD (Class 9 & 10)

69.03

6.98

0.87

Senior HSD (Class 11 & 12)

68.05

7.84

1.00

Bachelor’s Degree

71.40

6.97

0.89

Master’s Degree

70.93

7.56

0.68

SHS Factor

Primary Level (Class 1 - 8)

20.25

2.83

0.28

4/414

2.60

0.036* (0.024)

Junior HSD (Class 9 & 10)

20.48

3.14

0.39

Senior HSD (Class 11 & 12)

18.97

2.90

0.37

Bachelor’s Degree

19.87

2.98

0.38

Master’s Degree

19.90

2.81

0.25

MHLq Total

Primary Level (Class 1 - 8)

124.69

11.20

1.09

4/414

4.89

<0.001*** (0.045)

Junior HSD (Class 9 & 10)

129.85

9.75

1.21

Senior HSD (Class 11 & 12)

124.98

12.15

1.54

Bachelor’s Degree

129.44

11.03

1.40

Master’s Degree

129.96

12.09

1.09

Gender and Parental Education Interaction

HSFAS Factor

Female (Bachelor+)

38.94

4.84

0.59

7/136.17

1.72

0.11

Female (Senior HSD)

38.03

5.85

1.07

Female (Junior HSD)

40.27

3.92

0.68

Female (Primary)

40.04

4.35

0.59

Male (Bachelor+)

38.74

5.82

0.54

Male (Senior HSD)

37.91

5.17

0.91

Male (Junior HSD)

40.41

5.73

1.01

Male (Primary)

38

5.6

0.78

Continued

KSMHP Factor

Female (Bachelor+)

73.02

7.11

0.86

7/136.24

9.85

<0.001***(0.153)

Female (Senior HSD)

71.2

5.82

1.06

Female (Junior HSD)

71.55

6.7

1.17

Female (Primary)

66.78

6.93

0.94

Male (Bachelor+)

69.98

7.29

0.67

Male (Senior HSD)

65.09

8.41

1.49

Male (Junior HSD)

66.44

6.39

1.13

Male (Primary)

63.98

7.97

1.11

SHS Factor

Female (Bachelor+)

19.35

3

0.36

7/135.38

2.85

0.008**(0.044)

Female (Senior HSD)

19.27

2.16

0.4

Female (Junior HSD)

19.82

3.6

0.63

Female (Primary)

20.02

2.92

0.4

Male (Bachelor+)

20.2

2.75

0.25

Male (Senior HSD)

18.69

3.46

0.61

Male (Junior HSD)

21.16

2.46

0.44

Male (Primary)

20.48

2.74

0.38

MHLq Total

Female (Bachelor+)

131.31

11.01

1.34

7/136.92

4.64

<0.001***(0.075)

Female (Senior HSD)

128.5

9.55

1.74

Female (Junior HSD)

131.64

9.65

1.68

Female (Primary)

126.83

11.47

1.56

Male (Bachelor+)

128.91

12.07

1.11

Male (Senior HSD)

121.69

13.5

2.39

Male (Junior HSD)

128

9.67

1.71

Male (Primary)

122.46

10.56

1.47

*p < 0.05, **p < 0.01, ***p < 0.001. Note: The table includes means, standard deviations (SD), standard errors (SE), degrees of freedom (df), F-statistics, p-values, and partial eta squared (ηp2) for comparisons across ethnicity, grade levels, parental education and, gender and parental education interaction. Due to insufficient data points for individual categories, bachelor’s and master’s degrees were combined into the “Bachelor+” category for gender and parental education classifications. Effect sizes (ηp2) are shown for significant results.

No statistically significant differences were observed between participants permanently living inside the valley and those permanently living outside the valley in the level of MHL and its all the dimensions ( Table 2 ).

No statistically notable differences was observed between participants from government schools and private schools in the level of MHL and its dimensions ( Table 2 ).

We found no statistical difference in the level of MHL and its dimensions among participants from different grades ( Table 3 ).

A significant difference was observed among the levels of MHL based on the parental education i.e. primary level (Classes 1 - 8), junior HSD (Classes 9 & 10), senior HSD (Classes 11 & 12), bachelor’s degree and master’s degree ( Table 3 ). Games Howell post hoc analysis found the participants with parental education equals to junior HSD, bachelor or master’s degree had a significantly higher level of MHL than the participants with parental education equal to primary level ( Table 4 ). The difference was also significant among the groups in KSMHP and SHS dimensions ( Table 4 ).

We found a significant difference among the group variables in the interaction of gender and educational levels in the level of MHL, including the KSMHP and SHS dimensions ( Table 3 ). The post hoc analysis examined the interaction between gender and parental education across various factors. Significant differences were found between multiple groups, indicating variations in scores based on gender and parental education levels. Effect sizes ranged from small to large, highlighting the practical significance of these differences ( Table 4 ).

<xref ref-type="bibr" rid="scirp.141636-"></xref>Table 4. Games-howell post hoc comparisons for parental education and MHLQ.

95% CI for Mean Difference

Comparison

Mean Difference

Lower

Upper

SE

t

df

P-value (Cohen’s d)

Parental Education

KSMHP Factor

Bachelor’s Degree Primary Level (Class 1 - 8)

6.00

2.82

9.18

1.15

5.22

136.28

<0.001*** (0.81)

Master’s Degree Primary Level (Class 1 - 8)

5.52

2.77

8.27

1

5.52

222.48

<0.001*** (0.74)

Primary Level (Class 1 - 8) - Junior HSD (Class 9 & 10)

−3.63

−6.76

−0.49

1.14

−3.19

143.70

0.015* (−0.49)

SHS Factor

Senior HSD (Class 11 & 12) - Primary Level (Class 1 - 8)

−1.28

−2.55

−0.01

0.46

−2.78

125.32

0.048* (−0.44)

MHLQ Total

Master’s Degree Primary Level (Class 1 - 8)

5.27

1.04

9.50

1.54

3.43

226.53

0.006** (0.46)

Primary Level (Class 1 - 8) - Junior HSD (Class 9 & 10)

−5.16

−9.65

−0.67

1.63

−3.17

149.67

0.016* (−0.45)

Gender and Parental Education Interaction

KSMHP Factor

Female (Bachelor+) - Female (Primary)

6.24

2.29

10.18

1.28

4.88

115.05

<0.001*** (0.87)

Female (Bachelor+) - Male (Senior HSD)

7.92

2.50

13.34

1.72

4.61

52.63

<0.001*** (1.10)

Female (Bachelor+) - Male (Primary)

9.03

4.70

13.37

1.40

6.44

102.93

<0.001*** (1.26)

Continued

Female (Bachelor+) -Male (Junior HSD)

6.58

2.13

11.02

1.42

4.63

67.15

<0.001*** (0.92)

Female (Senior HSD) - Male (Senior HSD)

6.11

0.35

11.86

1.83

3.34

55.32

0.03* (0.85)

Female (Senior HSD) - Male (Primary)

7.22

2.44

12.00

1.53

4.71

75.50

<0.001*** (1.00)

Female (Primary) - Female (Junior HSD)

−4.77

−9.45

−0.08

1.50

−3.18

69.61

0.043* (−0.66)

Female (Junior HSD) - Male (Senior HSD)

6.45

0.52

12.39

1.89

3.42

59.17

0.024* (0.90)

Female (Junior HSD) - Male (Primary)

7.57

2.56

12.57

1.61

4.71

76.59

<0.001*** (1.05)

Female (Junior HSD) - Male (Junior HSD)

5.11

0.02

10.19

1.62

3.15

62.98

0.048* (0.71)

Male (Bachelor+) - Male (Primary)

5.99

1.98

10.01

1.29

4.64

90.14

<0.001*** (0.83)

SHS Factor

Female (Bachelor+) - Male (Junior HSD)

−1.80

−3.58

−0.03

0.57

−3.18

72.95

0.043* (−0.62)

Female (Senior HSD) - Male (Junior HSD)

−1.89

−3.74

−0.04

0.59

−3.21

59.76

0.041* (−0.65)

Male (Senior HSD) - Male (Junior HSD)

−2.47

−4.83

−0.11

0.75

−3.29

56.02

0.035* (−0.86)

MHLq Total

Female (Bachelor+) - Male (Senior HSD)

9.62

0.98

18.26

2.73

3.52

51.15

0.019* (0.86)

Female (Bachelor+) - Male (Primary)

8.85

2.72

14.97

1.98

4.46

112.08

<0.001*** (0.79)

Female (Junior HSD) - Male (Senior HSD)

9.95

0.76

19.13

2.92

3.41

56.01

0.025* (0.88)

Female (Junior HSD) - Male (Primary)

9.18

2.22

16.13

2.23

4.12

72.79

0.002** (0.82)

Male (Bachelor+) - Male (Primary)

6.45

0.76

12.13

1.84

3.51

110.63

0.015* (0.57)

*p < 0.05, **p < 0.01, ***p < 0.001. Note: The table shows mean differences, confidence intervals (95% CI), standard errors (SE), t-values, degrees of freedom (df), p-values, and effect sizes (Cohen’s d) for comparisons of parental education levels, and the combined effect of gender and parental education across factors.

 3.3. Recognition of Different Mental Health Conditions

A high proportion, 84.36%, reported awareness of depression, while generalized anxiety was recognized by 52.86%. In contrast, conditions like cerebral palsy, stroke, and trisomy showed low awareness levels, with only 14.54%, 13.22%, and 13.00% of participants recognizing these conditions, respectively. Awareness of Parkinson’s disease was particularly low at 8.81%. Additionally, 51.76% of participants were aware of schizophrenia ( Table 5 ).

<xref ref-type="bibr" rid="scirp.141636-"></xref>Table 5. Participants’ awareness of mental and neurological conditions.

Groups

Frequency

Percent

Generalized Anxiety

Yes

240

52.86

No

214

47.14

Depression

Yes

383

84.36

No

71

15.64

Cerebral palsy

Yes

66

14.54

No

388

85.46

Stroke

Yes

60

13.22

No

394

86.78

Trisomy

Yes

59

13.00

No

395

87.00

Schizophrenia

Yes

235

51.76

No

219

48.24

Parkinson

Yes

40

8.81

No

414

91.19

Note: The table shows the frequency and percentage of participants who reported awareness (“Yes”) or lack of awareness (“No”) of the listed conditions.

 4. Discussion

The study included 454 participants (56.82% male, 43.18% female) aged 12 to 16. Demographic variables included age, ethnicity, associated academic institutions, parental education, and family literacy measured by parental education levels. Participants’ awareness of mental health issues was also assessed. Data were collected from six schools across Kathmandu and Lalitpur Districts, including both public and private institutions.

We observed significantly higher levels of global mental health literacy and knowledge/stereotypes about mental health problems among females compared to males. This exactly aligns with the findings of Poudel et al. (2024) , who reported that females had greater awareness of erroneous beliefs/stereotypes, with the other dimensions remaining the same. Shrestha et al. (2023) reported that female students demonstrated significantly higher MHL, knowledge, first aid skills, and help-seeking behavior than male students, while erroneous beliefs and stereotypes were more prevalent among males ( Shrestha et al., 2023 ; Lee et al., 2020 ). Females scored higher on overall mental health literacy, showing greater knowledge and understanding across all dimensions except erroneous beliefs/stereotypes ( Dias et al., 2018 ). However, Mishra et al. (2023) found similar level of mental health literacy between female and male gender. Future research should explore the underlying factors contributing to these differences and develop targeted interventions to enhance mental health literacy among male students.

We observed a similar level of MHL based on the school type (government vs. private). However, students from community school had higher level of awareness in help seeking and first aid skills whereas lower level in knowledge/stereotypes about mental health problems. However, Poudel et al. (2024) found significantly higher level of self-help strategies in students from community colleges than in private colleges and government colleges. Siddique et al. (2022) found no differences according to the university type (public and private) in the level of mental health knowledge and awareness. These inconsistencies warrant further investigations to address these inconsistencies.

We found the significant influence of parental education on MHL. Adolescents whose parents had a higher education level (bachelor’s or master’s degree) demonstrated better MHL compared to those with parents educated only up to the primary level. This aligns with the idea that parental education, particularly the father’s education level, significantly influences mental health awareness ( Abonassir et al., 2021 ). Parental mental health literacy positively influenced adolescents’ mental health literacy, with parent-child intimacy mediating this relationship. Additionally, school mental health services moderated the links between parental literacy, intimacy, and adolescent literacy ( Wang et al., 2024 ). These dynamics, in turn, may facilitate knowledge sharing or discussions between parents and children. It is important for family members and the support network of individuals to possess skills that enable effective listening, providing support, and encouraging acknowledgment of the condition and seeking assistance ( Jorm, 2012 ). This suggests that school children and adolescents may benefit from school based mental programs, especially when parental involvement is taken into account.

Our study revealed that females with bachelor’s and junior high school parental education backgrounds demonstrated significantly higher mental health literacy specifically compared to males with senior high school and primary education backgrounds. The only significant difference among males was that those with bachelor’s level parental education showed better mental health literacy than those from primary education backgrounds. Our findings align with the evidence that a mother’s higher education contributes to greater mental health awareness ( Mishra et al., 2023 ). These findings suggest the need for targeted interventions to enhance mental health literacy, particularly among males from lower educational backgrounds. Additionally, females with higher parental education generally demonstrated better mental health literacy, particularly in knowledge/stereotypes about mental health problems. However, in self-help strategies, males from lower educational backgrounds showed higher competency than females from higher educational backgrounds, revealing a complex relationship between gender, parental education, and different aspects of mental health literacy, leaving room for future research.

No significant differences were found in MHL across ethnicity, permanent residence (inside vs. outside the valley), grades enrolled. This aligns with the previous study indicating non-significant differences based on the ethnicity and academic levels or literacy rate and academic years ( Jayan & Vishwas, 2023 ; Poudel et al., 2024 ; Siddique et al., 2022 ). However, Singh et al. (2013) found that adults residing in urban communities had greater knowledge of mental health and mental illness compared to those in rural communities. Studies have shown that academic levels or grade have significant impacts on mental health literacy. For example, Ayurveda students in Nepal showed high mental health literacy, increasing with educational level. Interns had the highest scores, highlighting the impact of advanced education ( Khayamali et al., 2023 ). The lack of significant variation in MHL across grades in this study may reflect the relatively narrow age range (12 - 16 years) and the homogeneity of the school curriculum in Nepal, which does not systematically address mental health education. Additionally, the absence of group differences combined with the low level of recognition of mental health issues may suggest a plateau in mental health literacy, highlighting the need for further examination and targeted improvement efforts.

Participants demonstrated high awareness of common mental health conditions like depression and generalized anxiety, consistent with findings that over 80% of students could identify these disorders ( Duwal et al., 2024 ; Khayamali et al., 2023 ). However, awareness of less common conditions, including Parkinson’s disease and cerebral palsy, was notably low. The varying levels of awareness across different mental health issues may result from the greater emphasis placed on common conditions, as highlighted by Jorm (2012) . Public health campaigns often prioritize widely recognized mental illnesses, such as anxiety and depression, potentially leaving gaps in awareness of neurological and developmental conditions, which warrants further exploration. Additionally, a significant portion of the population remains unaware of common mental health issues, such as anxiety, highlighting a lack of mental health literacy among school students which is consistent with mental health literacy among adolescents was low, with 29% recognizing depression and 1.31% aware of schizophrenia or psychosis ( Ogorchukwu et al., 2016 ). This aligns with findings from community surveys conducted in Australia, Canada, India, Japan, Sweden, the UK, and the US, which reveal widespread difficulties in accurately recognizing mental disorders ( Jorm, 2012 ). Our observation also revealed a notable variation in students’ awareness of depression and anxiety, highlighting the need for further exploration in this area.

 5. Conclusion

Our study found that females had higher mental health literacy (MHL) than males, particularly in knowledge and help-seeking behavior, while males showed more erroneous beliefs. Parental education positively influenced MHL, with higher literacy among students from more educated families. Males from lower parental educational backgrounds excelled in self-help strategies, suggesting a complex relationship between gender, parental education, and different aspects of mental health literacy. While awareness of common mental health conditions like depression and anxiety was high, knowledge of less common conditions was low, highlighting gaps in mental health education. These results emphasize the need for targeted interventions, especially for male students and those from lower educational backgrounds.

5.1. Implication

This study highlights the need for targeted interventions to improve mental health literacy (MHL) in male students, particularly those from lower parental education backgrounds. It emphasizes the importance of parental involvement and school-based mental health education. Future research should explore the complex relationship between gender, parental education, and MHL to develop more effective interventions.

 5.2. Future Directions

Future research should explore gender-based differences in mental health literacy, focusing on stereotypes and awareness of less common conditions like Parkinson’s disease. The role of parental education, parent-child intimacy, and school-based programs needs further investigation to enhance MHL. Additionally, examining MHL differences across public and private school students, particularly in help-seeking behaviors, and conducting longitudinal studies to assess the long-term effectiveness of current interventions are essential.

 Acknowledgements

We sincerely thank the Sambhavya Foundation for their support and extend our gratitude to the foundation members for their invaluable contributions. We also thank the school principals and students for their support and participation in this study. Additionally, we acknowledge all the authors cited in this article for their valuable contributions.

 Institutional Review Board Statement

We obtained permission from the school authorities from all the schools involved in this study. It followed the ethical guidelines of the Declaration of Helsinki, ensuring participants’ rights were protected throughout the research.

 Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

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