Short Communication - (2023) Volume 8, Issue 1
Received: 05-Jan-2023, Manuscript No. JFM-23-93460;
Editor assigned: 07-Jan-2023, Pre QC No. P-93460;
Reviewed: 19-Jan-2023, QC No. Q-93460;
Revised: 24-Jan-2023, Manuscript No. R-93460;
Published:
31-Jan-2023
, DOI: 10.37421/2472-1026.2023.8.190
Citation: Saras, Iyad. “Applications and Treatments of Psychological
Health and Drugs.” J Forensic Med 8 (2023): 190.
Copyright: © 2022 Saras I. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The sustainability of society can be negatively impacted by mental health problems on both people and communities. The treatment of mental disorders faces a number of difficulties, but it is more crucial to address their underlying causes because doing so can help prevent mental health issues from developing or returning. This necessitates a more comprehensive understanding of mental health issues than what has been found in previous studies. It is important to consider social and environmental aspects while analysing mental health. There is a need for more investigation and education, as well as for root cause-focused solutions. Medication dangers and effectiveness should both be researched. This study suggests a big data and machine learning-based method for automatically identifying mental health-related factors from Twitter data. 52 parameters in total were found for the three views. To combine relevant parameters, we established six macro-parameters. We give a thorough description of mental health, including its causes, medications and therapies, effects of drugs on the brain, and drug abuse, as discussed by the general people and medical professionals on Twitter. Additionally, we pinpoint their connections to other medicines. The research will pave the way for new methods of identifying drug addiction and use in relation to mental health on social media, as well as other micro- and macro-factors. The approach can be applied to different illnesses and offers the chance to find forensic toxicological evidence from social and digital media.
Drugs • Psychological health • Machine learning
Global catastrophes like the COVID-19 pandemic and environmental calamities, as well as growing social polarisation and division, are all elements that are leading to diminishing social sustainability [1]. These elements have had detrimental effects on our societies' current state and prospects for the future, which has resulted in a decline in social sustainability. Economic and environmental sustainability are closely related to social sustainability since they can both have a significant impact on a society's ability to provide for the needs of its constituents. It's critical to take action to address the underlying causes of these problems in order to reduce the danger of diminishing social sustainability.
Because it is a crucial component of total health and well-being and because mental health problems can have a large negative impact on both individuals and communities, mental health is related to social sustainability. Mental health conditions like depression and anxiety can have a severe impact on social and economic well-being by increasing absenteeism, decreasing productivity, and suicide. For instance, the World Health Organization (WHO) estimates that there are more than 700,000 suicides worldwide each year, one every 40 seconds. This high rate of suicide indicates the deteriorating social conditions around the world [2].
Moreover, addiction is often related to mental health in that it can be a symptom of, or a response to, underlying mental health issues. For example, people may turn to substances or behaviours such as drugs, smoking, drinking, gambling, or internet use as a strategy to cope with mental health concerns such as depression, anxiety, or stress. However, addiction can also contribute to, or exacerbate, mental health problems, as the use of substances or engagement in certain behaviours can have negative impacts on mental well-being [3].
The Centers for Disease Control and Prevention (CDC) reports that cigarette smoking results in more than 480,000 deaths annually in the United States, with over 40,000 of these deaths attributed to second-hand smoke exposure. Moreover, approximately 16 million Americans have endured severe health concerns owing to smoking [4]. Additionally, more than 19.5 million Americans over the age of 12 struggle with substance use disorders, according to the National Survey on Drug Use and Health (NSDUH) [5].
Access to care, stigma, a lack of mental health specialists, a lack of treatment alternatives, co-occurring illnesses, and a lack of integration with physical health care are just a few of the difficulties facing mental health treatment. These difficulties may make it difficult for people to get the mental health care they require, which may have a detrimental effect on their general well-being and quality of life. Promoting mental health and enhancing the wellbeing of people and communities require addressing these issues.
However, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring, improve the effectiveness of treatment, and reduce the need for ongoing care. Trauma, genes, the environment, and physical health conditions can all be root causes of mental health problems. To effectively address the underlying causes of mental health issues, a multifaceted strategy that takes into account individual needs as well as social, economic, and environmental factors is required.
In order to avoid mental health disorders, it is important to treat their underlying causes. Mental health concerns can have a substantial impact on both individuals and communities. Using the big data and machine learning method outlined in this paper, it is possible to automatically identify parameters pertaining to mental health from Twitter data, such as details on medications and treatments, causes and effects, and drug abuse. This can give a thorough understanding of mental health as it is discussed by the general public and medical professionals, seen on social media, and associated with various drugs. The approach can be applied to additional diseases and has the potential to glean forensic toxicology evidence from social and digital media. More research is necessary to fully investigate the potential of social media for forensic reasons; this report is just the beginning and it will determine our future work.
The conclusions drawn in this work are confirmed by scientific data and objectivity in several ways. The paper first recognises mental health as a serious problem that has an impact on people and communities and emphasises the need for a comprehensive strategy to comprehend its underlying causes. This perspective is reinforced by scientific studies on mental health and the impacts of social and environmental factors on mental health outcomes. Second, we suggest a data-driven method for extracting mental health-related parameters from Twitter data. The use of big data and machine learning techniques is a well-established methodology for data analysis and can bring insights into complicated topics, including mental health. Third, we present the findings of this investigation, which gathered more than a million Arabic tweets about mental health in Saudi Arabia. The use of Twitter data offers an unbiased and current view of mental health issues as they are discussed by the general public and medical professionals. An extensive account of mental health, its causes, treatments, and interactions between drugs and the mind is given in the analysis of the Twitter data.
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Journal of Forensic Medicine received 165 citations as per Google Scholar report
