Abstract:In this new era that is full of social changes, ongoing economic transformation, an abundance of information resources, and a fast pace of life, the pressure that people feel to compete with one another is also increasing day by day. Because of the vast differences in people’s states of consciousness and worldviews, interpersonal relationships have become increasingly difficult to navigate. Students in higher education institutions will eventually emerge as the dominant demographic in society. Their mental hea… Show more
“…In addition, the results of the study indicate that discriminant analysis and principal component analysis have yielded fruitful interpretations of clustering results. The TwoStep algorithm is an enhanced hierarchical clustering algorithm that decreases the algorithm’s temporal complexity, automatically determines the ideal cluster number, and scales well ( Dong & Shen, 2022 ).…”
The dynamic landscape of public health occurrences presents a formidable challenge to the emotional well-being of college students, necessitating a precise appraisal of their mental health (MH) status. A pivotal metric in this realm is the Mental Health Assessment Index, a prevalent gauge utilized to ascertain an individual’s psychological well-being. However, prevailing indices predominantly stem from a physical vantage point, neglecting the intricate psychological dimensions. In pursuit of a judicious evaluation of college students’ mental health within the crucible of public health vicissitudes, we have pioneered an innovative metric, underscored by temporal perception, in concert with a hybrid clustering algorithm. This augmentation stands poised to enrich the extant psychological assessment index framework. Our approach hinges on the transmutation of temporal perception into a quantifiable measure, harmoniously interwoven with established evaluative metrics, thereby forging a novel composite evaluation metric. This composite metric serves as the fulcrum upon which we have conceived a pioneering clustering algorithm, seamlessly fusing the fireworks algorithm with K-means clustering. The strategic integration of the fireworks algorithm addresses a noteworthy vulnerability inherent to K-means—its susceptibility to converging onto local optima. Empirical validation of our paradigm attests to its efficacy. The proposed hybrid clustering algorithm aptly captures the dynamic nuances characterizing college students’ mental health trajectories. Across diverse assessment stages, our model consistently attains an accuracy threshold surpassing 90%, thus outshining existing evaluation techniques in both precision and simplicity. In summation, this innovative amalgamation presents a formidable stride toward an augmented understanding of college students’ mental well-being during times of fluctuating public health dynamics.
“…In addition, the results of the study indicate that discriminant analysis and principal component analysis have yielded fruitful interpretations of clustering results. The TwoStep algorithm is an enhanced hierarchical clustering algorithm that decreases the algorithm’s temporal complexity, automatically determines the ideal cluster number, and scales well ( Dong & Shen, 2022 ).…”
The dynamic landscape of public health occurrences presents a formidable challenge to the emotional well-being of college students, necessitating a precise appraisal of their mental health (MH) status. A pivotal metric in this realm is the Mental Health Assessment Index, a prevalent gauge utilized to ascertain an individual’s psychological well-being. However, prevailing indices predominantly stem from a physical vantage point, neglecting the intricate psychological dimensions. In pursuit of a judicious evaluation of college students’ mental health within the crucible of public health vicissitudes, we have pioneered an innovative metric, underscored by temporal perception, in concert with a hybrid clustering algorithm. This augmentation stands poised to enrich the extant psychological assessment index framework. Our approach hinges on the transmutation of temporal perception into a quantifiable measure, harmoniously interwoven with established evaluative metrics, thereby forging a novel composite evaluation metric. This composite metric serves as the fulcrum upon which we have conceived a pioneering clustering algorithm, seamlessly fusing the fireworks algorithm with K-means clustering. The strategic integration of the fireworks algorithm addresses a noteworthy vulnerability inherent to K-means—its susceptibility to converging onto local optima. Empirical validation of our paradigm attests to its efficacy. The proposed hybrid clustering algorithm aptly captures the dynamic nuances characterizing college students’ mental health trajectories. Across diverse assessment stages, our model consistently attains an accuracy threshold surpassing 90%, thus outshining existing evaluation techniques in both precision and simplicity. In summation, this innovative amalgamation presents a formidable stride toward an augmented understanding of college students’ mental well-being during times of fluctuating public health dynamics.
“…In an era where environmental issues are becoming more pressing, individual understanding and action towards sustainability is essential (Knerer, Currie, and Brailsford 2020). Health education programs have been an effective channel for communicating information about health and promoting positive behavior change (Dong and Shen 2022). However, so far, the role of health education programs in shaping environmentally responsible attitudes in students still needs to be detailed (Tryana Ramadhany Batubara 2022).…”
Health education programs play an important role in shaping students' attitudes and behaviors towards various aspects of health and well-being. This study aims to analyze the economic implications and management strategies of health education programs in shaping students' environmentally responsible attitudes. Environmental responsibility has become an important concern globally, and education is recognized as a key factor in creating awareness and internalizing sustainable behavior. This research adopted a mixed-methods approach, combining quantitative analysis of economic impacts with qualitative examination of management strategies. The quantitative aspect of the research involved assessing the cost-effectiveness of the health education program, taking into account the resources invested, the benefits derived, and the potential long-term savings due to behavior change. This economic analysis provides insight into the financial viability and potential return on investment of such a program. A qualitative phase explored the management strategies used in a health education program to encourage the formation of environmentally responsible attitudes. Through interviews, surveys, and content analysis, this study investigates how program design, communication methods, resource allocation, and stakeholder engagement contribute to program effectiveness. Results from this study are expected to reveal the economic benefits of health education programs that promote environmentally responsible attitudes in students. In addition, the findings will highlight management strategies that successfully increase the impact and sustainability of the program. By comprehensively understanding the economic dimensions and management aspects, educational institutions and decision-makers can make informed decisions regarding resource allocation and strategy implementation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.