Exploring the Path of Higher Vocational Students’ Mental Health Education in the Perspective of Social Support Theory

With the development of society, people pay more and more attention to mental health. As a part of college students, the mental health of higher vocational students is also a matter of concern. With the continuous development of higher vocational education, students’ mental health problems have become more and more prominent and need to be solved urgently. Therefore, strengthening students’ mental health education has become an important task of higher vocational education [1-4].

Firstly, students’ mental health education can help cultivate students’ correct learning attitudes and methods, improve their learning motivation and learning effect, and help them better adapt to the learning environment and cope with academic challenges. Secondly, mental health education can cultivate students’ positive, friendly and cooperative emotional attitudes, improve their interpersonal skills and social adaptability, and promote the formation of good interpersonal relationships [5-8]. In addition, mental health education can improve students’ psychological quality and stress resistance, so that they can effectively cope with all kinds of difficulties and setbacks, and maintain psychological balance in the face of pressure. Mental health education of students in higher vocational education is an educational work to improve the comprehensive quality of students, promote the overall development of students, and cultivate good psychological quality [9-12]. In front of the pressure of students, adolescent higher vocational students are especially vulnerable, if there is not a good state of mental health, there will be low learning efficiency, self-discipline and other unfavorable phenomena of growth. With mental health education, students can correctly recognize the psychological problems and can take more effective ways when solving problems [13-16].

Literature [17] examined the data mining technique based on literature review and analyzed its application in the data of psychological problems of higher education students. Through experiments, it was concluded that this technology provides help for students’ mental health education, counseling and prevention, and enhances the purpose, relevance and effectiveness of mental health education. Literature [18], in order to solve the mental health problems of students, discusses that learning should increase policy support and strengthen the development of psychological counseling capacity. It also proposes strategies to address the problems of mental health education such as formalism and imperfect system. Literature [19] points out that at present, students in higher vocational colleges and universities generally have psychological problems, and it is urgent to reform the status quo of mental health education. Based on the analysis of the status quo of health education for higher vocational students in Shanghai, a feasible reform program is proposed. Literature [20] emphasizes the importance of psychological education for higher vocational students and discusses the prevalence, specificity and complexity of mental health problems of higher vocational students. By describing the problems exposed during the implementation of mental health education, countermeasures are proposed in terms of innovative educational methods. Literature [21] has established a “four-guide integrated” system of teacher guidance, curriculum leadership, monitoring and navigation, and hotline escort in mental health education, which effectively builds a long-term mechanism for eliminating the phenomenon of “four deficiencies”. And by guiding students to cultivate positive psychological quality, laying the foundation for the cultivation of modern construction talents. Literature [22] pointed out the importance of psychological services to improve the comprehensive quality of students and the existence of imbalance in the allocation of resources in the psychological service system, organizational structure is not perfect and other problems. Emphasize that colleges and universities can only change this situation by adopting corresponding response strategies. And IPcomputer analysis software was adopted to study the psychological health status of higher vocational students. Literature [23] discusses the Web platform technology in conjunction with the principles of creating a Web platform-based health system that is tailored to the needs of the students and student-centered. The aim is to improve the effectiveness of the application of the Web platform-based health management system to enhance the mental health of higher education students. Literature [24] examined the psychological level of students and the opportunities and challenges encountered in education in the context of the Internet, and explored innovative mental health education models and methods based on this. Literature [25] reveals the importance of psychological establishment education for higher vocational students. It also emphasizes that improving the mental health education level of higher vocational students cannot be separated from perfect classroom education, excellent campus culture and practical psychological counseling work. Literature [26], in order to improve the mental health level of higher vocational students and effectively help them develop morally, intellectually and physically in an all-round way, discusses the measures to solve these problems in the light of the mental health status and problems of higher vocational students. Literature [27] specifies the important role of mental health education in the field of education in China, and studies the impact of the combination of positive psychology and mental health education on the mental health of vocational school students. Literature [28] describes the viewpoints for better development of mental health education for college students based on the analysis of the current situation of mental health of students in higher vocational colleges and the current situation of mental health education in schools.

Under the perspective of social support theory, this study formulated effective psychological counseling strategies for better mental health education of higher vocational students, including strengthening communication between home and school, improving the curriculum system of mental health education, cultivating students’ peer counseling ability and integrating social support resources. The AHP-fuzzy comprehensive evaluation method was also used to construct an index system for evaluating the effect of mental health education for higher vocational students, and the effect of mental health education for higher vocational students was empirically analyzed. The system was applied to the quality evaluation survey of the teaching of student mental health education courses in School M to verify the feasibility of the proposed path.

2

Presentation of mental health education pathways and evaluation methods

2.1

Counseling Strategies for Higher Vocational Students under Social Support Theory

The psychological counseling strategies for higher vocational students under the social support theory proposed in this study, including strengthening communication between home and school, improving the curriculum system of mental health education, cultivating students’ peer counseling ability, and integrating social support resources, aim to provide comprehensive psychological support for higher vocational students, and to provide a scientific basis and a practical guide for the exploration of the path of mental health education for higher vocational students.

2.1.1

Building family support systems

1)

Enhance communication between home and school to increase attention and support.

(1)

Carry out face-to-face parent meetings in the middle of the semester to provide parents with opportunities for one-on-one exchanges with teachers. A psychological counseling area is set up at the parent meeting site, where professional psychological teachers provide parents with counseling services and guidance suggestions.

(2)

Build a home-school information sharing system. With the help of an APP or online platform, parents can learn in real time about the school’s psychological counseling course schedule, students’ psychological assessment results and other information, and at the same time, parents can upload their students’ psychological observation records at home, so as to realize a two-way flow of information.

2)

Carry out family mental health education activities.

(1)

Organize family mental health lectures, inviting well-known psychologists to give lectures to parents on common psychological problems of college students.

(2)

Design a family psychological counseling manual, which covers mental health knowledge, parent-child communication skills, and ways to cope with psychological problems;

2.1.2

Strengthening of school support systems

1)

Improve the mental health education curriculum system.

(1)

Enrich the content of the curriculum. In addition to traditional theoretical knowledge, increase the practical operation sessions. The curriculum should also cover cutting-edge psychological research results and actual cases to enhance the attractiveness and practicality of the program;

(2)

Adopt diversified teaching methods, introducing experiential teaching in addition to classroom lectures. At the same time, online and offline blended teaching will be carried out, with independent learning resources provided online, including psychological tests and online tutoring, and centralized lectures and interactive exchanges conducted offline;

2)

Build a professional psychological counseling service team. Schools should deploy a diversified psychological service team consisting of counselors, psychologists, and guidance counselors to ensure that every student receives timely and effective psychological support. Provide multi-channel services such as telephone counseling and face-to-face meetings to meet the counseling needs of different students.

3)

Create a positive psychological culture on campus.

(1)

Organize various kinds of mental health promotion activities, such as Mental Health Week, theme lectures, etc., to raise students’ awareness of and attention to mental health;

(2)

Strengthen cooperation with internal and external media, and utilize channels such as campus radio and the school newspaper to disseminate knowledge about mental health, so as to form a campus-wide good atmosphere of concern and support for mental health.

2.1.3

Expanding peer support systems

1)

Cultivate students’ peer counseling skills. Higher vocational can design a set of systematic peer psychological counseling training program, the training content should cover the basic psychological theory and the basic process of crisis intervention. The training process is divided into two stages: theoretical learning and practical operation.

2)

Establish peer support groups. According to the interests and needs of students, different thematic mutual support groups are established, and group members carry out regular activities to enhance mutual understanding and improve psychological quality by sharing experiences, discussing problems, and playing psychological games.

2.1.4

Integration of social support resources

1)

Strengthen cooperation with communities and social organizations. Specific operational steps:

(1)

Identify and contact local active community and social organizations;

(2)

Co-design and implement mental health programs for college students;

(3)

Establish a regular mechanism for information exchange and resource sharing.

In addition, the cooperation should also include opportunities for community service for higher education students. Through participation in community service, students can learn how to manage their emotions and stress in the process of service, and achieve self-growth and mental health improvement.

2)

Introducing social professional psychological service organizations. Schools and the introduced agencies work together to formulate personalized psychological counseling programs and design targeted counseling courses and activities for the characteristics and needs of students of different grades and majors. The organization provides regular feedback to the school on the progress and problems of the students, while the school adjusts its teaching and management strategies according to the feedback.

2.2

AHP-Fuzzy Comprehensive Evaluation Method for Mental Health Education

2.2.1

Hierarchical Analysis

Hierarchical analysis (AHP for short) is a decision-making method that decomposes the relevant elements into several levels and carries out qualitative and quantitative analysis on top of that. And this paper’s research on the evaluation of higher vocational students’ mental health education pathway adopts the hierarchical structure model. This means that by using the hierarchical analysis method, it is easier to derive the weights of the indicators and calculate the quality evaluation of the mental health education path for higher vocational students. Theoretically, the hierarchical analysis method has a more rigorous logical and theoretical foundation, which includes both qualitative analysis and can ensure quantitative analysis. Therefore, the ratings calculated through this method are more credible. The steps of its utilization are as follows.

1)

Establish a reasonable hierarchical model

When using the AHP method for problem solving, the logical steps of the problem need to be sorted out clearly, so that a reasonable hierarchical model can be divided. In the process of analyzing the problem, it is necessary to understand the essence of the problem first, so as to find the most critical part - to determine the evaluation objectives. Then, according to the actual situation, the evaluation objectives are divided into a number of levels from top to bottom, so as to build a hierarchical structure model. Generally speaking, it can be divided into the following three levels, and the hierarchical structure is shown in Figure 1.

(1)

Goal layer: the goal layer is generally located in the top layer, which is the final task we need to accomplish. In this paper, the objective layer is “evaluation of mental health education path for higher vocational students”. Usually, the objective layer is an indicator.

(2)

Guideline layer: The guideline layer is between the program layer and the target layer. It is set up to verify whether the objective layer can achieve the desired goals.

(3)

Program Layer: The Program Layer is located at the lowest level and exists to refine the Objective Layer. Usually it is some specific program choices.

2)

Construct judgment matrix

The judgment matrix is a method for determining the weight of each indicator element. The principle is that the importance of each indicator in the guideline and program levels are compared with each other to construct the judgment matrix $A = {(A_{i j})}_{n \times n}$ . Commonly used methods include the 1-9 scale method, i.e., the numbers 1-9 and their reciprocals are used to express the relative importance. The contents of each numerical representation are shown in Table 1.

The first step is to collect the relative importance scoring of the indicators by relevant experts and scholars, and the judgment matrix $A = {(A_{i j})}_{n \times n}$ can be constructed according to the relative scoring value of each indicator, as shown in Table 2. Where $A_{i j}$ indicates the importance score of factor $i$ relative to factor $j$ .

3)

Hierarchical single sort

The calculation process of hierarchical single sort is to calculate the maximum eigenvalue of the actual judgment matrix and the corresponding eigenvariable, and finally normalize it to get the eigenvector. The goal of hierarchical sorting is to sort the corresponding factors of the same level by calculating the weights of the relative importance of the corresponding factors of the previous level.

(1) $A W = W λ_{\max}$

In Equation (1) $A$ is the judgment matrix, $λ_{max}$ is the maximum eigenvalue, and $W$ is the eigenvector after normalization.

The weight obtained according to the arithmetic mean method is $ω_{i}$ , see formula (2): (2) $ω_{i} = \frac{1}{n} \sum_{j = 1}^{n} \frac{a_{i j}}{\sum_{k = 1}^{n} a_{k j}}$

4)

Consistency test

People’s perceptions of things tend to vary according to their own experiences and the knowledge they have received, thus showing diversity.

So the judgment matrix constructed by different people its difficult to reach a consistent standard, so it is necessary to carry out consistency test to ensure the reliability of its calculation results. After passing the test, the weight vector is the normalized eigenvector. If the consistency test is not passed, it proves that the judgment matrix is unreasonable and needs to be reconstructed and calculated. In order to carry out the test, it is necessary to calculate the size of the value of $C . I .$ to measure the consistency of the judgment matrix, the smaller the value is the more desirable, see formula (3), where $n$ represents the judgment matrix order and $λ_{\max}$ is the indicator weight vector.

(3) $C . I . = \frac{λ_{\max} - n}{n - 1}$

The values of consistency may vary in matrices of different orders, and to measure the magnitude of $C . I .$ , a random consistency index is introduced $R . I .$ , The value of $R . I .$ is obtained by constructing 500 positive reciprocal inverse matrices using a randomized method of drawing numbers from 1-9 and their reciprocal to obtain the average of the largest eigenroot ${λ^{'}}_{\max}$ , $R . I .$ is calculated as shown in Equation (4), and the R.I. values are shown in Table 3.

(4) $R . I . = \frac{λ_{\max} - n}{n - 1}$

The ratio of $C . I .$ to $R . I .$ , i.e., the consistency indicator $C . R .$ , is used to indicate whether the judgment needs to be changed, as in Equation (5). When $C . R . < 0.1$ , the consistency test passes, and when $C . R . \geq 0.1$ , it fails and the judgment matrix needs to be adjusted or even reconstructed.

(5) $C . R = \frac{C . I .}{R . I .}$

Table 1.

Determine the value and meaning of the matrix

The value of a_ij	Scale definition
1	A_i and A_j are the same important
3	A_i and A_j are slightly more important
5	A_i and A_j are more important
7	A_i and A_j are very important
9	A_i and A_j are extremely important
2, 4, 6, 8	The median of the above pairwise importance scores

Table 2.

Judgment matrix

Y	X₁	X₂	……	X_n
X₁	A₁₁	A₁₂	……	A_1n
X₂	A₂₁	A₂₂	……	A_2n
……	……	……	……	……
X_n	A_n1	A_n2	……	A_nb

Table 3.

Average random consistency index $R . I .$

Rank	1	2	3	4	5	6	7	8	9
	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45

2.2.2

Fuzzy integrated evaluation method

In daily life to evaluate a thing, often the thing involved in a variety of factors will be considered, but many problems are not simple to quantify. Therefore, the conventional multi-indicator evaluation method can not effectively solve the problem and give objective conclusions, and the fuzzy comprehensive evaluation method can better help in this field.

The method is based on the theory of fuzzy mathematics, the objectives to be evaluated are decomposed into a number of sets, which are used to deal with some factors that are difficult to be quantified and to analyze the subordinate rank of the evaluation object in many ways. The steps of its application are as follows: 1)

Determine the factors or indicators of the thing being evaluated Thesis $U = \{U_{1}, U_{2}, \dots \dots, U_{n}\}$ ;

2)

Determine the rubric rating thesis domain $V = \{V_{1}, V_{2}, \dots \dots, V_{m}\}$ . e.g. $V = \{V_{1}, V_{2}, V_{3}\}$ . $V_{1}$ for good, $V_{2}$ for fair, and $V_{3}$ for poor;

3)

Determine the affiliation matrix. For any $u \in U$ , there is a number $U_{A} (u) \in [0, 1]$ corresponding to it, which indicates the degree of affiliation to $U$ , referred to as the degree of affiliation. The mapping $U_{A}$ is also known as the affiliation function of $A$ . Then the affiliation matrix $R_{A} = [r_{i j}]$ of $U$ to $V$ is shown in equation, where $r_{i j}$ represents the degree of affiliation of the $i$ th factor to the $j$ th rubric; (6) $R_{A} = [\begin{matrix} r_{A_{1}}^{1} & r_{A_{1}}^{2} & \dots & r_{A_{1}}^{j} \\ r_{A_{2}}^{1} & r_{A_{2}}^{2} & \dots & r_{A_{2}}^{j} \\ \dots & \dots & ⋱ & \dots \\ r_{A}^{1} & r_{A_{1}}^{2} & \dots & r_{A_{1}}^{j} \end{matrix}]$

4)

Determine the evaluation factor weight vector $A = \{a_{1}, a_{2}, \dots \dots, a_{n}\}$ , $a_{j}$ represents the weight of factor $U_{1}$ , which satisfies $\sum_{j = 1}^{n} a_{j} = 1$ ;

5)

Select the comprehensive evaluation operator and get the comprehensive evaluation vector through relevant calculations. Assuming that the comprehensive weight of the first level indicator is $ω_{A} = (ω_{A 1}, ω_{A 2}, ω_{A 3}, ω_{A 4})$ , the corresponding evaluation level affiliation matrix calculation method is shown in Equation (7). Where $G_{A}$ represents the evaluation results, $R_{A}$ represents the evaluation grade affiliation matrix of evaluation indicators, and “ $\cdot$ ” represents the fuzzy synthesis operator.

(7) $\begin{matrix} G_{A} = ω_{A} \cdot R_{A} = [\begin{array}{l} ω_{A 1} & ω_{A 2} & ω_{A 3} & ω_{A 4} \end{array}] \cdot [\begin{matrix} r_{A_{1}}^{1} & r_{A_{1}}^{2} & \dots & r_{A_{1}}^{j} \\ r_{A_{2}}^{1} & r_{A_{2}}^{2} & \dots & r_{A_{2}}^{j} \\ \dots & \dots & ⋱ & \dots \\ r_{A}^{1} & r_{A}^{2} & \dots & r_{A_{1}}^{j} \end{matrix}] \\ = [\begin{array}{l} G_{A}^{1} & G_{A}^{2} & G_{A}^{3} & G_{A}^{4} \end{array}] \end{matrix}$

6)

The score is calculated as shown in equation (8).

(8) $F_{i} = G_{i} \cdot S^{T}$ where $F_{i}$ represents the overall evaluation score and $S^{T}$ represents a vector of columns of quantitative evaluation ratings.

2.2.3

AHP-fuzzy comprehensive evaluation method

Combining the hierarchical analysis method with the fuzzy comprehensive evaluation method, the AHP-fuzzy comprehensive evaluation method is obtained. First, using hierarchical analysis, invite relevant experts or practitioners to score the relative importance of indicators at all levels in the indicator system, and then construct a judgment matrix to calculate the weight of each indicator, and then bring the weight of the obtained indicators to the fuzzy comprehensive evaluation method to get the evaluation results. The utilization flow chart is shown in Figure 2.

The method integrates the advantages of the two methods, combining qualitative research and quantitative calculation. Its use in the evaluation of the path of mental health education for higher vocational students can not only reduce the disadvantages of personal subjectivity involved in the evaluation, but also reflect the accuracy of the evaluation factors, and can effectively solve the fuzzy problems encountered in the evaluation process. It is more objective and scientific than the single two evaluation models.

3

Construction and application of a system of indicators for evaluating the effectiveness of mental health education

3.1

Composition of the evaluation indicator system

Mental health practice ability is an important indicator of the development status of mental health literacy of higher vocational students, and it is a key target for carrying out mental health education of higher vocational students. During the period of senior vocational education, students are faced with increasingly complicated living environments, and the problems they need to solve gradually increase, focusing on four aspects: learning, interpersonal, emotional and employment; therefore, the mental health practice ability that senior vocational students need to focus on is mainly including: learning ability, environmental adaptability, interpersonal relationship ability, and employment ability.

First, learning ability refers to students’ state of maintaining learning motivation and learning efficiency. Students are in an energetic learning stage, they should exert their own subjective ability, actively explore the fun in learning, challenge themselves, and continuously improve their learning level in order to obtain more knowledge and a sense of success.

Secondly, the ability to adapt to the environment means that students can effectively cope with the complex and changing environment and can correctly recognize and deal with the relationship between themselves and the environment. In a changing environment students should proactively adapt to changes so as to be able to better meet the requirements of their surroundings and to maintain good communication with the society so as to have a clearer and more accurate understanding of the social status quo.

Thirdly, interpersonal ability is the key to measure whether students have good interpersonal communication ability and maintain good interpersonal relationship. Individual interpersonal relationship situation can best reflect and mirror the psychological health of individuals. At the university stage, most of the students experience dormitory life for the first time, and dealing with dormitory relationship, collective relationship and teacher-student relationship is a significant purpose for college students to improve their interpersonal skills. Individuals with good interpersonal skills are willing to interact with others, respect, trust, care and understand their classmates, maintain good friendship with mutual sharing and acceptance, maintain synergistic and progressive relationship with the collective, and actively communicate and communicate with their teachers.

Fourth, employability is an important indicator of students’ comprehensive quality. The employment situation faced by most college graduates is more complicated than “employment difficulties”, they don’t know which career they should choose, which one is more suitable for them, and what are the characteristics of each career, which have all become a big challenge for them. Through mental health education, college students can better establish their career aspirations, and can more accurately carry out self-assessment and career planning, so as to enhance their employment competitiveness.

According to the above analysis, the evaluation index system of the effect of mental health education is shown in Table 4.

Table 4.

Mental health education effect evaluation index system framework

Evaluation object	Primary index	Secondary index
Effect of mental health education B	Mental health knowledge cognition B₁	Mental health basic knowledge cognition C₁₁
		Category recognition of psychological abnormalities C₁₂
		Raise awareness of mental health methods C₁₃
	Mental health ideology B₂	Group particularity consciousness C₂₂
		Mental problem consciousness C₂₃
		Mental health awareness C₂₄
	Mental health practice ability B₃	Learning ability C₃₁
		Environmental adaptability C₃₂
		Interpersonal skills C₃₃
		Employ ability C₃₄

3.2

Weighting of evaluation indicators

Hierarchical analysis is often used to set the weights of indicators, and this study will use the decision-making method based on AHP-Fuzzy Comprehensive Evaluation Method to decompose the relevant elements of decision-making into objectives, guidelines, programs and so on, and on the basis of which, the decision-making method combining qualitative and quantitative analysis will be carried out. The main purpose is to mathematize the decision-making thinking process by constructing a model with a hierarchical structure, and finally to carry out the solution of the research problem.

3.2.1

Constructing a hierarchical analytical structure

The hierarchical decomposition structure is constructed through the preliminary setting of the evaluation index system of the effect of mental health education for higher vocational students. The target layer is the status quo of mental health education effect level of higher vocational students; the guideline layer is the intermediate link to realize the predetermined, i.e., to reflect the overall level of students’ mental health education effect through the level of knowledge cognition, ideological awareness and practical ability. The program layer is a specific program that indicates the solution of the problem, i.e., it specifically reflects the current status of the effect of college students’ mental health education through the cognition of the basic knowledge of mental health, cognition of psychological abnormal categories, cognition of the methods to enhance mental health; awareness of group specificity, awareness of psychological problems, awareness of mental health; formation of learning ability, environmental adaptability, interpersonal communication ability, and employability.

3.2.2

Constructing judgment matrices

Constructing judgment matrix is the key to carry out the AHP-fuzzy comprehensive evaluation method, through the expert survey method, firstly, design the expert questionnaire according to the determined indicators, select eight experts in the relevant fields (education economics and management, student management, mental health), and after the experts define the corresponding indexes, get the scores of each expert on the importance of the evaluation indexes. The initial measurement of obtaining the weights of the indicators is carried out, and subsequently, on the basis of the initial measurement, the scores of some of the indicators are adjusted by combining the opinions of the relevant experts, so as to determine the final results, and the judgment matrix about the evaluation of the effect of higher vocational students’ mental health education is finally collated and obtained as shown in Table 5.

Table 5.

Student mental health education evaluation index discriminant matrix

B	B₁	B₂	B₃
B₁	1	5	3
B₂	1/5	1	1/2
B₃	1/3	2	1
B₁	C₁₁	C₁₂	C₁₃
C₁₁	1	2	4
C₁₂	1/2	1	3
C₁₃	1/4	1/3	1
B₂	C₂₁	C₂₂	C₂₃
C₂₁	1	1/4	2
C₂₂	4	1	1/3
C₂₃	1/2	3	1
B₃	C₃₁	C₃₂	C₃₃	C₃₄
C₃₁	1	1/5	2	1/3
C₃₂	5	1	1/3	4
C₃₃	1/2	3	1	4
C₃₄	3	1/4	1/4	1

The judgment matrix is a matrix formed by judging the relative importance of each factor in each level to obtain a numerical value. Assuming n elements, the two-by-two judgment matrix B=(B_ij)n×n, where factor i and factor j indicate the degree of importance relative to the goal B. For example, for target B,B₁₂ indicates that for the effect of mental health education for higher vocational students, the mental health knowledge cognitive factor is more important than the mental health ideology factor, while for target B₂, C₂₃ indicates that for the mental health ideology, the psychological problem awareness factor is much more important than the mental health awareness factor.

3.2.3

Consistency test

Hierarchical single ranking is a method of comparing weights in the AHP-Fuzzy Comprehensive Evaluation Method, whereby the factors of the previous level are compared with the factors of the current level in order to confirm the uniformity between them. In order to ensure uniformity, a consistency test must be applied to the comparison matrix to confirm the degree of inconsistency.

First, the consistency is determined by first calculating the maximum eigenvalue of the indicator judgment matrix.

(9) $λ_{\max} = \sum_{i = 1}^{n} \frac{{(A W)}_{i}}{n W_{i}}$

Next, the consistency indicator CI needs to be calculated.

(10)

C I = \frac{λ_{\max} - n}{n - 1}

The larger the value of CI, the worse the matrix consistency, and vice versa, the better the matrix consistency, and the stochastic consistency ratio CR is calculated by comparing it with the average stochastic consistency index RI (e.g., Tables 6 to 9), and when the CR is less than 0.1, it can be assumed that the judgment matrix is reasonably constructed and has consistency.

Table 6.

Determine the matrix B₁-B₃ consistency test results

	B₁	B₂	B₃	Consistency check
B₁	1	5	3	$C R = \frac{C I}{R I} = 0.021 < 0.1$ $$CR = {{CI} \over {RI}} = 0.021 < 0.1$$
B₂	1/5	1	1/2
B₃	1/3	2	1

Table 7.

Determine the matrix C₁₁-C₁₃ consistency test results

	C₁₁	C₁₂	C₁₃	Consistency check
C₁₁	1	2	4	$C R = \frac{C I}{R I} = 0.009 < 0.1$ $$CR = {{CI} \over {RI}} = 0.009 < 0.1$$
C₁₂	1/2	1	3
C₁₃	1/4	1/3	1

Table 8.

Determine the matrix C₂₁-C₂₃ consistency test results

	C₂₁	C₂₂	C₂₃	Consistency check
C₂₁	1	1/4	2	$C R = \frac{C I}{R I} = 0.058 < 0.1$ $$CR = {{CI} \over {RI}} = 0.058 < 0.1$$
C₂₂	4	1	1/3
C₂₃	1/2	3	1

Table 9.

Determine the matrix C₃₁-C₃₄ consistency test results

	C₃₁	C₃₂	C₃₃	C₃₄	Consistency check
C₃₁	1	1/5	2	1/3	$C R = \frac{C I}{R I} = 0.075 < 0.1$ $$CR = {{CI} \over {RI}} = 0.075 < 0.1$$
C₃₂	5	1	1/3	4
C₃₃	1/2	3	1	4
C₃₄	3	1/4	1/4	1

3.2.4

Calculation of indicator weights

The calculation of indicator weights is carried out in AHP-Fuzzy Comprehensive Evaluation Method, in which the sum-product method is a method to carry out the ranking of optimization problems, so this paper adopts the sum-product method to carry out the calculation of indicator weights. In this paper, the weights of each indicator of the evaluation of the effect of mental health education for higher vocational students are calculated by online SPSS software, as shown in Table 10.

Table 10.

Student mental health education effect evaluation index weight

Primary index	weighted value	Secondary index	weighted value
Mental health knowledge cognition B₁	0.625	Mental health basic knowledge cognition C₁₁	0.578
		Category recognition of psychological abnormalities C₁₂	0.224
		Raise awareness of mental health methods C₁₃	0.198
Mental health ideology B₂	0.113	Group particularity consciousness C₂₂	0.143
		Mental problem consciousness C₂₃	0.731
		Mental health awareness C₂₄	0.126
Mental health practice ability B₃	0.262	Learning ability C₃₁	0.495
		Environmental adaptability C₃₂	0.194
		Interpersonal skills C₃₃	0.176
		Employ ability C₃₄	0.135

3.3

Empirical analysis of mental health education quality evaluation

In this study, a senior vocational school in Guangdong Province (referred to as School M) was selected to conduct a questionnaire survey on the mental health education program for senior students in School M, and then the evaluation results were analyzed.

3.3.1

Basic information

School M is a key senior vocational school in Guangdong Province, after communication, a class of 50 students was selected in the school, and the counseling strategy proposed in section 2.1 was applied for a period of 3 months, after which the “Teaching Quality Evaluation Indicator System for Higher Vocational Students’ Mental Health Education Course” was applied as a questionnaire to conduct a random sampling survey. A total of 50 questionnaires were distributed and recovered. A total of 50 questionnaires were distributed and 48 questionnaires were collected, with a recovery rate of 96%. The questionnaire is in the form of Likert table, and Table 11 is used to convert the scores. Firstly, the scores of all the experts on the secondary indicators are added up and averaged, multiplied by the weights of the corresponding secondary indicators, and two decimals are retained to obtain the score of the secondary indicators; finally, the scores of the secondary indicators under the first-level indicators are added up and multiplied by the weights of the corresponding first-level indicators to obtain the score of the first-level indicators.

Table 11.

Data conversion

Option	Excellent	Good	Medium	Qualified	Poor
Score	5	4	3	2	1

3.3.2

Results

Eventually, the scores of the indicators of the teaching quality evaluation index system of the students’ mental health education program in the target classes are shown in Table 12.

Table 12.

Statistical table of scores of primary and secondary indicators

Primary index	Score	Secondary index	Score
Mental health knowledge cognition B₁	4.00	Mental health basic knowledge cognition C₁₁	4.23
		Category recognition of psychological abnormalities C₁₂	3.76
		Raise awareness of mental health methods C₁₃	3.63
Mental health ideology B₂	3.02	Group particularity consciousness C₂₂	2.97
		Mental problem consciousness C₂₃	3.12
		Mental health awareness C₂₄	2.49
Mental health practice ability B₃	3.32	Learning ability C₃₁	3.54
		Environmental adaptability C₃₂	3.07
		Interpersonal skills C₃₃	2.91
		Employ ability C₃₄	3.40

The final result of the evaluation of the teaching quality of the mental health education program for higher vocational students is the sum of the scores of all the first-level indicators * the corresponding weights = mental health knowledge cognitive score * 0.625 + mental health ideology score * 0.113 + mental health practice ability score * 0.262 = 3.71.

By comparing the data conversion table 11 results in 3-4, is “good” range, indicating that this paper builds the mental health education path has been verified.

4

Conclusion

This study used the AHP-fuzzy comprehensive evaluation method to construct an index system for evaluating the effect of mental health education for higher vocational students, and empirically analyzed the effect of students’ mental health education to verify the feasibility of this mental health path under the social support theory. Subsequently, this system was used to apply in the quality evaluation survey of the teaching of students’ mental health education program in M school, and the conclusions are as follows.

1)

The judgment matrix for evaluating the effect of mental health education for higher vocational students shows that for the effect of mental health education for higher vocational students, the factor of mental health knowledge cognition is more important than the factor of mental health ideology; for mental health ideology, the factor of psychological problem awareness is much more important than the factor of mental health awareness.

2)

Using the AHP-fuzzy comprehensive evaluation method, the test shows that the CR values of students’ mental health knowledge cognition, mental health ideology awareness, and mental health practice ability are all less than 0.1, which proves that the judgment matrix for evaluating the effectiveness of mental health education for higher vocational students is constructed reasonably and has consistency.

3)

The teaching quality evaluation score of the mental health education program for students in school M is 3.71, which proves that the path is feasible, reasonable and effective.

In conclusion, the path of students’ mental health education under the social support theory proposed in this paper demonstrates its feasibility through a series of empirical analyses, and provides supportive guidance for the improvement of higher vocational students’ mental health education.

Lingua:: Inglese

Frequenza di pubblicazione:: 1 volte all'anno
Argomenti della rivista:: Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro

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Exploring the Path of Higher Vocational Students’ Mental Health Education in the Perspective of Social Support Theory

Jinli Ye

Pubblicato online: 23 set 2025

Ricevuto: 06 gen 2025

Accettato: 18 apr 2025

DOI: https://doi.org/10.2478/amns-2025-0986

Parole chiaveSocial support theory, Mental health education, AHP-fuzzy comprehensive evaluation method, Judgment matrix, Course teaching quality evaluation

© 2025 Jinli Ye, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Social support theory, Mental health education, AHP-fuzzy comprehensive evaluation method, Judgment matrix, Course teaching quality evaluation