Suicide and attempted suicide are among the leading causes of morbidity and mortality in the fields of mental and public health. All industrialized countries, including the United States, agree that suicide is the second leading cause of death among young people. According to the most up-to-date CDC and NCHS Data brief for 2019, suicide is the second leading cause of death for those between the ages of 15 and 24. According to the most up-to-date CDC statistics, the suicide rate in the United States as a whole rose by 30% between 2000 and 2016. This upward trend was seen across all age groups. Public and mental health experts agree that adolescent suicidality is a major issue that must be addressed by today's suicide prevention programmed. Stress in interpersonal relationships, social isolation, mental illness, substance abuse, and addiction may all contribute to suicide ideation and behaviour. Hence, it is essential to have a firm grasp of the many elements that may contribute to adolescent suicidality. The authors have made an effort to summarise the most important protective and risk variables in this area, based on the results of the scientific literature across the disciplines of biology, neurochemistry, neurobiology, and neuroimaging. Also, they aimed to construct an explanatory integrative model for suicidality by reviewing clinical studies on the topic, with a particular emphasis on suicide diagnosis, prevention, and therapy. The World Health Organization reports that adolescent suicide has gradually increased over the last half century, making it the second leading cause of death worldwide, behind accidents. These disturbing statistics highlight the critical need to investigate and address the causes of suicide, particularly among young people. The risk of suicide ideation and behaviour in adolescents is greatly amplified when depressive symptoms and feelings of isolation coexist. However, there are just a few of markers that, taken together, may reliably foretell suicide thoughts or the transition to an actual attempt. Studying the neurodevelopmental aspects that contribute to suicidal ideation and behaviour might improve suicide prediction and treatment (Medrano, 2020).

Suicide is second only to accidental death among those aged 10 to 24. Recognizing risk factors throughout adolescence is important because of the rise in suicidal ideation and behaviour (STB) among this age group. Yet, few theories of suicide really deal with adolescent difficulties, and most research just extend adult models to children. This has the potential to be helpful, but it also poses a barrier to research into the processes that are more strongly linked to STBs among adolescents and that may play a more substantial role in the occurrence of suicide among this age group. Decades of study have shown that well-studied demographic and clinical indicators are not beneficial for predicting suicide-related outcomes beyond chance. In order to improve STB prevention, there has been a need for research into additional transdiagnostic suicide risk factors. The RDoC framework focuses on aspects that are common to many distinct mental symptoms and represents these dimensions as constructs at several levels of analysis. Furthermore, it has considerable potential for shedding light on hitherto unidentified risk factors that might serve as focal areas for preventive and therapeutic efforts (Stewart, 2019).

BACKGROUND OF THE STUDY:

Suicide is a complex process that is influenced by a wide range of factors, both internal and external. The higher risk of suicide within families where SB is widespread makes it important to identify factors that may contribute to the familial transmission of SB. More focus in recent years has been on the need of improving neurocognitive skills. Neurocognitive deficits may be used as an endophenotype for suicide risk. In addition, several investigations have shown a connection between SB and neurocognitive impairments, including a decline in attention. Suicidal thoughts are more likely among those with poor attentional control, according to research on the ability to concentrate on a single task at a time. Adults and adolescents with SB show higher sensitivity to these elements, as well as a lower distractibility threshold and enhanced emotional sensitivity in processing environmental stimuli. Studies have linked poor organisational techniques used during initial encoding of information and difficulties with retrieval to an increased risk of suicide in those with impaired memory function issues with emotion regulation, problem solving, and behavioural inhibition have all been linked to deficits in executive function, which in turn increases susceptibility to SB. Researchers have shown that those who have suicidal thoughts or who have tried suicide have unique difficulties with cognitive inhibition and decision making. When SB moves from theory to practice, it may encompass several fields. Together, these findings suggest a link between cognition and SB and emphasise the necessity to uncover changes in cognitive processes linked with SI. In a massive multi-site longitudinal study called the Adolescent Brain Cognitive Development ABCD StudyVR, children aged 9 and 10 are observed from the onset of puberty until early adulthood. The ABCD project hopes, in part, to analyse how early life experiences influence subsequent health and achievement. Hence, the ABCD study provides data for examining risk factors for suicide thinking and behaviour in young people. This study examined the link between SI and cognitive ability using baseline data from the ABCD project (data version 2.0.1). As just one study has been conducted on the topic of SI and neurocognition in children, the researchers used an exploratory analytic approach and evaluated the participants' performance on a variety of cognitive tasks. The following findings are based on previous studies including both young people and adults. A neurocognitive advantage was predicted for children with SI over those without SI on tests of attention, memory, and executive function (Huber, 2020).

PROBLEM STATEMENT:

“Suicide is the leading cause of death among adolescents. While clinicians and researchers have begun to recognize the importance of considering multidimensional factors in understanding risk for suicidal thoughts and behaviors (STBs) during this developmental period, the role of puberty has been largely ignored.”

This study by Stoep examined adolescence is a time of transition between childhood and maturity, marked by changes in a person's physical, psychological, social, and neurological development. Neurohormones play a major role in this period of life, regulating a number of important physiological and psychological processes that ultimately lead to full maturation for a life of hard work and reproduction. Furthermore, living with family and peer groups has a significant impact on shaping a person, as does the essential integration into the social environment (Stoep, 2016).

RESEARCH OBJECTIVE:

• To evaluate the leading cause of suicide in adolescents.

• To identify the causes of cognitive dissonance associated with suicidal thought.

• To find the level of effect of cognitive disorders on adolescents suicides.

• To learn about the mental illness those are associated with the highest risk of suicide among adolescents.

LITERATURE REVIEW:

Suicidal thoughts and actions are strongly linked to serious depression in adolescents. Thus, they must have an in-depth knowledge of the factors that raise the risk of suicidal ideation and behaviour among youth. Researchers have collated and critically evaluated the literature on clinical and neurobiological factors in adolescent suicide in this review. The explanatory model for suicidal behaviour that they are studying connects clinical and psychological risk factors to the underlying neurobiological and cognitive difficulties associated with suicidal behaviour, and it has the potential to aid in the discovery of treatment options and has empirical value. They argue that changes in suicidal thinking and behaviour may result from a confluence of developmental, biological (genetics, proteomics, epigenetics, immunology), psychological/clinical (childhood traumas), and environmental factors. As a result, their working hypothesis is to provide a more all-encompassing framework for understanding suicidality by elucidating the link between several sets of neurobiological, genetic, and clinical data in the area. Prevention measures for suicide ideation and behaviour must take into account the many interrelated risk variables, including those that are psychological, biological, sociobiological, and clinical in nature. Furthermore, they contrast the neurological abnormalities identified in adults with those seen in suicidal adolescents. Research on the neurobiology of suicide should centre on either the living brain or the brain of a deceased person, notwithstanding the limitations of the latter. Future research should zero in on identifying the specific mechanisms at work in the emergence of suicidal thoughts and behaviour in young individuals. With this information, physicians might better detect suicidal teenagers and treat them based on factors including the adolescent's impulsivity, aggressiveness, poor positive affect, social isolation, high negative thoughts, low distress tolerance, and contacts with their family. This knowledge will help them develop more effective strategies for preventing and responding to suicidal behaviour among young people (Medrano, 2020).

Effective suicide prevention measures need an understanding of the interplay between psychological, biological, sociobiological, and clinical risk factors. They also investigate for neurological similarities or differences between suicidal tendencies in adults and those of adolescents. Yet, studies of the brain or the brain after death are more suited to studying the neurobiology of suicide. Adolescent suicidal thoughts and behaviour need further research into its root causes in the future. Learning this would be a huge asset. Understanding the progression of the condition, obtaining an accurate diagnosis, and administering treatments that focus on the teen's impulsivity, aggression, poor positive affect, isolation, high levels of negative thinking, inability to deal with pain, and family relationships are all crucial. This kind of information will aid doctors in coming up with more effective strategies for dealing with suicidal thoughts and actions among young people. Depressive disorders, especially severe depression, are a leading cause of suicidal ideation and behaviour in young people. So, they need a thorough understanding of the factors that raise young people's suicidal tendencies. To better understand how to intervene with suicidal patients, the authors conducted a comprehensive literature review centred on the explanatory model for suicidal behaviour, which links clinical and psychological risk factors to the underlying neurobiological, neuropsychological abnormalities related to suicidal behaviour. In support of their claims, they provide examples from their own lives. The authors postulate that differences in suicidal behaviour may be impacted by biology (genetics, proteomics, epigenetics, immunology), psychological/clinical (childhood traumas), environmental (developmental), and social variables. Hence, their model synthesises data from neurobiological, genetic, and clinical studies of suicidality to try to explain the observed association between these several types of data (Anderson, 2017).

Sampling: : The subjects in this study were 1329 adolescents sampled from the total population of China.

Data and Measurement: The data were collected during the first half of the annual year 2022. Adolescents were required. Questionnaire was distributed and quantitative analysis was implemented.

Statistical Software: MS-Excel and SPSS 25 Was used for Statistical analysis.

Statistical Tools: Descriptive analysis Was applied to understand the basic nature of the data. Validity and reliability of the data Was tested through Cronbach alpha.

RESULT:

Factor Analysis

Factor analysis is often used to verify the latent component structure of a set of measurement items (FA). It is believed that latent (or unseen) factors account for the observed (or measured) scores. Modeling is at the heart of accuracy analysis (FA). It focuses on modelling the interplay of seen occurrences, undiscovered causes, and measurement error. The Kaiser-Meyer-Olkin (KMO) Test may be used to determine whether the data is suitable for factor analysis. Both individual model variables and the whole model are tested to ensure sufficient sampling. Data analysis reveals the extent to which many variables may have some common variance. In most cases, a lower proportion indicates that the data is more amenable to factor analysis. KMO returns values between zero and one. The sample size is adequate if and only if the KMO value is between 0.8 and 1.0. A KMO of less than 0.6 indicates inadequate sampling and calls for adjustment. Some authors utilise the number 0.5 for this purpose; somewhere between 0.5 and 0.6, they'll have to use their discretion. KMO If it's close to zero, then means the sum of the correlations is tiny compared to the size of the partial correlations. To restate, large-scale correlations are a significant obstacle to component analysis. Here are Kaiser's minimum and maximum standards: Kaiser's minimum and maximum standards are as follows. Faltering between 0.050 and 0.059.

Below-average (0.60-0.69) In the middle school level, typically, With a quality point value between 0.80 and 0.89. Incredible diversity exists between 0.90 and 1.00.

KMO and Bartlett's Test:

Assessing the data's suitability for factor analysis is the first step in exploratory factor analysis (EFA). According to Kaiser's proposal, the KMO (Kaiser-Meyer-Olkin) measure of sample adequacy coefficient value must be more than 0.5 in order to conduct factor analysis. To explain, KMO is an abbreviation for the Kaiser-Meyer-Olkin measure of the statistical significance of a sample. In this study, the KMO value for the information used was .964. Bartlett's test of sphericity also indicated a 0.00 significance level.

Test for Hypothesis

In scientific discourse, "posing a hypothesis" refers to the act of putting out a guess or assumption for the purpose of further debate and, ultimately, testing to determine how likely it is that the guess or assumption is correct. After developing a working hypothesis, the next stage in the scientific process is to conduct a literature review. The results were exactly what was predicted by the hypothesis. It is considered a hypothesis when it is a suggested answer to the study's main problem. Depending on the scope of the study, it may be required to develop a large number of hypotheses, each of which would be put to the test.

Dependent Variable:

Suicide in Adolescents:

Taking one's own life is the definition of suicide. It may provide relief to those who are going through anguish or anguish-inducing experiences. They state that someone "died by suicide" when they take their own life by saying that they "committed suicide." Someone who makes an effort to take their own life but is unsuccessful is said to have "attempted suicide." A person commits suicide when they end their own life by taking their own life or by doing actions that would cause them to die. Those who commit suicide are almost always suffering from severe depression and mistakenly feel that this is the only way to end their suffering.

Factor:

Impulsivity:

Children that are impulsive cannot control their actions even knowing that doing so might endanger themselves or have unfavourable consequences. Controlling one's impulses is the mental equivalent of a "stop" sign. Lack of self-control makes it hard for a child to stop and think before acting.

Independent Variable:

Cognitive Brain Change:

The most significant changes that occur in cognition as a natural part of ageing are declines in performance on cognitive tasks that require one to quickly process or transform information in order to make a decision. These changes include declines in measures of speed of processing, working memory, and executive cognitive function.

The relationship between impulsivity and suicide in adolescents.

Suicide is the second leading cause of death among adolescents, and impulsivity has recently been recognised as a possible marker of risk for this devastating epidemic. The present study set out to answer the question of whether or not there is a differential association between impulsivity and suicidal ideation, planning, and behaviour. Participants were then given self-report questionnaires to complete after interviews in order to gauge the intensity of their symptoms and impulsive tendency.

On basis of the above discussion, the researcher formulated the following hypothesis, which was analysed the relationship between impulsivity and suicide in adolescents.

H₀₁: “There is no significant relationship between impulsivity and suicide in adolescents.”

H₁:  “There is a significant relationship between impulsivity and suicide in adolescents.”

Correlations

Doing a multiple regression analysis in SPSS Statistics produced many output tables. Assuming that none of the assumptions were violated, this part solely discusses the three crucial tables that are required to adequately understand the outcomes of the multiple regression technique that was employed to analyse their data. This method was used to their company's data. While analysing their data for the eight assumptions required to do multiple regression, it is important to understand the conclusion, and this study, which is included in their enlarged lesson, provides a thorough description of what must be done. Before beginning the multiple regression process, a number of conditions must be satisfied. The Model Summary table is the first one that demands attention. To evaluate the accuracy of a regression model, they may consult this table, which includes the R, R2, modified R2, and standard error of the estimate.

Model Summary

The number in the "R" column is the multiple correlation coefficient. R may be used to assess the accuracy with which the dependant variable, in this case disruptive innovations, is predicted. Hence, a score of 1.0 denotes an appropriate level of prediction. The "R Square" column displays the "coefficient of determination," sometimes known as the R2 number. The percentage of total variance in the dependant variable that can be assigned to the effect of the independent variables is shown in this chart, which is used to infer causal linkages (technically, it is the proportion of variation accounted for by the regression model above and beyond the mean model). Given that their value is 1.0, it may be assumed that their independent factors adequately account for the fluctuation in their dependant variable, which is the emergence of disruptive technologies. Yet, they must understand the "Adjusted R Square" well in order to present their results in a professional way (adj. R2). In an improved multiple regression course, researchers discuss both the results and the conditions that led to these findings.

The "R" column displays the value for the multiple correlation coefficient (R). The predictive accuracy of the dependant variable, in this case disruptive innovations, may be evaluated using R. This illustration demonstrates the acceptability of a forecast accuracy of 1.0. The F-ratio (R2) is shown in the "R Square" column of the analysis of variance (ANOVA) table. The regression model as a whole closely approximates the data if this value is high. The table shows that there is a very significant predictive relationship between the independent factors and the dependant variable (F (5, 94) = 10496673816440674, p.0005). (In other words, the regression model adequately accounts for the data.) The basic equation that may be used to anticipate disruptive technology based on Impulsivity, Negative thinking patterns, Perfectionism, Rumination: The likelihood of including essential components, Suicide in Adolescents= 1.677+ (9.343E-7 x H1_Mean (Impulsivity)) 

CONCLUSION:

Adolescents who have experienced are more likely to develop and treat mental health problems later in life, have trouble making ends meet financially, socially, and physically, engage in dangerous or criminal activities, and are more likely to get involved in other  themselves. Hence, preventing the advent of would save a costly, lifelong impairment. Adolescents' use and the neurocognitive processes discussed in this review (cognitive control, reward responsiveness/valuation, and negative urgency) are only just beginning to be studied, and existing studies often find contradictory results. These neurocognitive processes need further attention because they may serve as early indications of suicide risk. In particular, studies have shown that unaffected people with a high risk for STBs have deficiencies in specific cognitive control skills and reward responsiveness that are comparable to those seen in kids with current . What's more, their findings may influence innovative therapies as they learn more about the specific neuropsychological traits of adolescents at high risk for . Recent research by Peckham and Johnson, for instance, has shown that people who are particularly prone to emotion-related impulsivity may benefit from a 6-session cognitive control training programme that helps them rein in their impulses. Ultimately, there is great potential for enhancing adolescent safety and well-being via the use of research-informed neurocognitive processes to detect high-risk children and pre-teens and to inspire innovative and successful early treatments.

LIMITATION:

In quantitative techniques, mathematical models, equations, and other mathematical expressions are utilised; they are based on a set of assumptions. These presumptions may or may not be relevant to other situations. If this warning is disregarded, the wrong use of quantitative methods might have disastrous results. Since they sometimes need the insight of specialised individuals, quantitative methods may be quite expensive. As many applications are not worth the costs, even large companies only use quantitative methodologies occasionally. Managers typically make decisions based on their intuition and past experiences rather than cold, hard facts. Quantitative methods may not always provide the most precise results due to common pitfalls include a lack of data, inconsistent definitions, a bad choice of sample, an inappropriate method, inappropriate comparisons, and poor presentation. Because of their inability to account for qualitative phenomenon's inherent intangibles and non-measurable human characteristics, quantitative methodologies cannot be used to analyse qualitative phenomena. The methods, for instance, do not take into consideration intangibles like a manager's ability, attitude, or excitement. Indirectly, though, the strategies might be put into action via the process of translating qualitative claims into numerical ones. A manager's Level of intelligence, for instance, may be evaluated on the basis of how highly they rated specific personal traits.

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