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Home Our Mission Corruption Files Articles Report Corruption/Volunteer How to purchase a Congressperson for Peanuts September 28, 2023 DeSantis in Decline: President Donald Trump, and the decline of traditional Republicanism September 21, 2023 New Way to Prevent Criminal Justice Involvement for People with Mental Illness September 18, 2023 How to purchase a Congressperson for Peanuts September 28, 2023 Top Menu “Like all Americans, I am sick and tired of the corruption, and I want it to be exposed.” Congressperson Kat Cammack In 2023, the Peanut industry emerged as one of the most heavily subsidized sectors in the United States. The subsidy program, providing over $800 million annually, benefits approximately 6,500 "Farms." These businesses, typically affluent financially secure "Farms" are concentrated among a relatively small number of wealthy individuals or corporations. The peanut shelling industry is dominated by two powerful companies that together buy 80 percent of all peanuts grown in the U.S. This monopoly power controls pricing in the industry. Subsidizing the peanut industry cost U.S. taxpayers more than $2 billion from 2014 through 2018. It is the most costly per-acre crop to taxpayers in America, in large part because monopoly power controls pricing in the industry. This can distort the market, creating an uneven playing field that makes it challenging for family farms to compete. Even worse during hard times, smaller family farms often struggle to stay afloat without the financial support of Congress these Corporations are receiving, and as a result, they fail and are bought up by larger corporations, with taxpayers money. This has created the consolidation of the agricultural industry and worsens the family farms struggle by the redistributing of wealth from taxpayers money to large corporations. It makes it difficult, if not impossible, for smaller family farms to compete. As large Corporations buy up smaller family farms, the number of independent farms declines. This reduces competition and makes it more difficult for family farms to get a fair price for their peanuts. This is nothing more than a redistributing of wealth from Congress to large corporations which means that taxpayer money is not being used to support small family farms but Corporations. Congressperson Kat Cammack has been a strong advocate for the Peanut Subsidies. “In #FL03, we are all about peanuts.” Congressperson Kat Cammack According to the USDA, the number of farms in the U.S. declined by 3% between 2012 and 2017, while the average farm size increased by 2%. The share of farms operated by corporations rose from 1.6% in 2012 to 1.8% in 2017, while the share of farms operated by families fell from 97% to 96%. Despite this shift, family farms still make up a significant portion of U.S. agriculture, accounting for 86% of farm production in 2017. However, the largest 15 percent of farm businesses receive over 85 percent of farm subsidies, and just ten percent of America's largest and richest farms collect almost three-fourths of federal farm subsidies. This indicates that a significant portion of agricultural subsidies is going to large and corporate farms, even though most of the agriculture is produced by small family farms. Congressperson Kat Cammack has been a strong advocate for the peanut industry and its interests. She has supported the Farm Bill, which provides funding for various agricultural programs, including peanut subsidies. She has also opposed foreign peanut subsidies that she claims harm domestic producers and consumers. She has stated that "food security is national security," highlighting the critical role of agriculture in maintaining the nation's wellbeing. But these subsidies are unfair to taxpayers, we are essentially paying for large corporations to get even richer. Peanut subsidies distort the market, making it difficult for smaller family farms to compete. Peanut subsidies are not even necessary to protect the peanut industry, as peanuts are a relatively profitable crop. Then why is Congressperson Kat Cammack a strong advocate for the Peanut Subsidies? “We must work towards draining the self-serving bureaucracy out of the Washington swamp." Congressperson Kat Cammack The peanut subsidy program makes it difficult, if not impossible, for smaller family farms to compete. The subsidies allow larger farms to produce peanuts at a lower cost, which gives them an unfair advantage in the marketplace. The peanut programs focus on large farms contributes to the corporate consolidation of American agriculture. In other words, the peanut subsidy program is a way for wealthy individuals and corporations to purchase politicians with taxpayer money. They donate to the campaigns of politicians who will support the subsidy program, and in return, they receive billions of dollars in subsidies each year. Further Reading The Peanut Program: An Unwarranted Windfall for Wealthy Farmers, a report by the American Enterprise Institute that analyzes the costs and benefits of the peanut subsidy program and its impact on the market and consumers. Peanut Subsidies in the United States, a report by the Environmental Working Group that provides data on the distribution and recipients of peanut subsidies from 1995 to 2020. The Farm Bill: A Citizen's Guide, a book by Daniel Imhoff and Christina Badaracco that explains the history, politics, and economics of the Farm Bill and its implications for food, farming, and the environment. DeSantis in Decline: President Donald Trump, and the decline of traditional Republicanism September 21, 2023 Top Menu Since 2016, the Republican Party has shifted significantly to the right, with most Republicans now supporting populist, nationalist, and anti-establishment views. Governor Ron DeSantis of Florida is widely seen as a potential successor to Trump. DeSantis has been a close ally of Trump, endorsing him in 2016 and 2020, and adopting many of his policies and positions on issues such as immigration, health care, education, and the coronavirus pandemic. DeSantis has also gained popularity among Republican voters for his handling of the pandemic, which has been praised by some for being pro-business and pro-freedom and criticized by others for being reckless and irresponsible. However, DeSantis's approval rating among Republican voters has declined in recent months, according to a Harvard CAPS / Harris Poll conducted in May 2023. The poll found that only 52% of Republican voters approve of the job DeSantis is doing as governor, down from 63% in January 2023. On March 26, 2023, former president Donald Trump delivered a fiery speech at a rally in Texas, where he attacked his former ally and potential rival, Florida governor Ron DeSantis. Trump accused DeSantis of being weak on immigration, crime, and COVID-19, and mocked his weight and appearance. The speech was widely seen as a sign of Trump's intention to run for president again in 2024, and a challenge to DeSantis. The event marked a turning point in the Republican primary race, and the beginning of a bitter feud between the two men. DeSantis's popularity has taken a hit since March 26, when he also signed a controversial bill that restricted voting rights in Florida. The poll shows that his net favorability rating among all voters dropped from +11 to -4, a 15-point swing in less than a month. A recent Harvard CAPS / Harris Poll found that the top issues of concern for American voters are the economy, health care, and climate change. All three of these issues are areas where DeSantis has been criticized for his record. According to the poll, 59% of voters say that curbing inflation is a top priority, followed by balancing the budget/tightening government spending (57%), encouraging domestic oil and gas exploration (60%), reducing China tariffs (58%), and lowering interest rates (57%). The same poll found that DeSantis received less than 10% confidence among Republican voters in these areas. Being these are all areas of particular interest to Republican voters, suggesting that DeSantis's record on these issues could be a liability for him in the future. It remains to be seen whether DeSantis can overcome his recent decline in popularity and become the Republican nominee for president in 2024. However, it is clear that he faces significant challenges, both from Trump and from other Republicans who are seeking the nomination. All data used to evaluate Governor Ron DeSantis can be found here Harvard CAPS / Harris Poll Please feel free to download, and share any insights you may have. A New Way to Prevent Criminal Justice Involvement for People with Mental Illness: Using Prescription Data to Predict Mental Health Needs September 18, 2023 Top Menu Mental illness is a major public health problem, with one in five adults in the United States experiencing a mental illness each year. People with mental illness are also more likely to come into contact with the criminal justice system than those without mental illness. In fact, one-third of all jail inmates have a serious mental illness. One way to reduce criminal justice involvement for people with mental illness is to develop predictive models that can identify those who are at risk for a mental health crisis. These models could be used to target preventive interventions, such as increased access to mental health care or support groups. **Thesis statement:** This paper proposes a new policy recommendation for using prescription data to predict mental health needs and reduce criminal justice involvement for people with mental illness. This paper is divided into six sections: Section II: The Current State of Mental Health Treatment This section provides an overview of current methods in mental health treatment, the role of prescription adjustments in mental health treatment, and the limitations and challenges of the current approach. Section III: The Intersection of Mental Health and Criminal Justice This section discusses the criminalization of mental illness, provides case studies of mental illness treatment in the criminal justice system, and argues for the need for a mental health response rather than a law enforcement response. Section IV: Predictive Learning Models in Mental Health This section provides an overview of existing learning models in predicting mental health needs, discusses the potential benefits and limitations of these models, and provides case studies or examples of successful implementation. Section V: Policy Recommendations This section proposes developing learning models based on prescription adjustment requests and integrating these models into the criminal justice system. It also discusses potential challenges and solutions in implementing these policies. Section VI: Conclusion This section recaps the issues discussed in the paper and the proposed solutions. It also considers the potential impact on patients, healthcare providers, and the criminal justice system, and offers final thoughts and future directions. In addition to the six main sections, the paper also includes a list of references for further reading and citation. Section II: The Current State of Mental Health Treatment The current state of mental health treatment is complex and multifaceted. There is no one-size-fits-all approach, and the best treatment plan for any individual will vary depending on their specific needs. However, some of the most common methods of mental health treatment include: Psychotherapy: Psychotherapy, also known as talk therapy, is a type of treatment that involves talking to a mental health professional about your thoughts, feelings, and experiences. Psychotherapy can help you to understand your mental health condition better, develop coping mechanisms, and make positive changes in your life. Medication: Medication can be a very effective way to treat many mental health conditions. However, it is important to note that medication should not be used alone and should always be used in conjunction with psychotherapy. Lifestyle changes: Lifestyle changes, such as getting regular exercise, eating a healthy diet, and getting enough sleep, can also play a role in improving mental health. The role of prescription adjustments in mental health treatment Prescription adjustments are a common part of mental health treatment. Sometimes, people with mental health conditions need to have their medication dosage adjusted to achieve the best possible results. Prescription adjustments can also be necessary if a person is experiencing side effects from their medication. It is important to note that prescription adjustments should only be made by a qualified mental health professional. There is no one-size-fits-all approach to prescription adjustments, and the best way to determine the right dosage for any individual will vary depending on their specific needs. Limitations and challenges in the current approach The current approach to mental health treatment has several limitations and challenges. One challenge is that there is a shortage of mental health professionals, which can make it difficult for people to get access to the care they need. Additionally, mental health care can be expensive, and not everyone has access to affordable coverage. Another challenge is that the stigma associated with mental illness can prevent people from seeking help. People with mental illness may be afraid of being judged or discriminated against, which can lead to them delaying or avoiding treatment. Finally, it is important to note that mental health conditions are complex, and there is no one-size-fits-all treatment approach. This can make it difficult to find the right treatment for any individual, and it may take some time and experimentation to find what works best. Despite these challenges, there has been significant progress in the field of mental health treatment in recent years. Researchers are developing new and more effective treatments, and there is a growing awareness of the importance of mental health care. With continued progress, we can hope to create a future where everyone has access to the care, they need to live a healthy and fulfilling life. Section III: The Intersection of Mental Health and Criminal Justice Mental illness is a major public health problem, but it is also a criminal justice problem. People with mental illness are more likely to be arrested, incarcerated, and subjected to police brutality than people without mental illness. This is due to a number of factors, including: Lack of access to mental health care Misunderstanding and stigma surrounding mental illness Law enforcement officers' lack of training in dealing with people with mental illness Case studies of mental illness treatment in the criminal justice system There are many cases of people with mental illness being mistreated in the criminal justice system. For example: In 2016, Eric Garner, a man with schizophrenia, was killed by police in Staten Island, New York, after he was placed in a chokehold. Garner had been repeatedly telling officers that he could not breathe, but they ignored him. In 2017, Tanisha Anderson, a woman with bipolar disorder, was killed by police in Cleveland, Ohio, after they were called to her apartment because she was having a mental health crisis. Anderson was unarmed and was shot multiple times by police. In 2020, Daniel Prude, a man with schizophrenia, was killed by police in Rochester, New York, after he was placed in a mesh hood and hogtied. Prude died of asphyxiation. These are just a few examples of the many cases of people with mental illness being mistreated and killed by police. The need for a mental health response rather than a law enforcement response When people with mental illness are in crisis, they need a mental health response, not a law enforcement response. Law enforcement officers are not trained to deal with people with mental illness, and their interactions with people with mental illness often lead to violence and death. There are a number of things that can be done to reduce the criminalization of mental illness and improve the treatment of people with mental illness in the criminal justice system. These include: Increasing access to mental health care Educating law enforcement officers about mental illness Establishing mental health courts Diverting people with mental illness away from the criminal justice system and into mental health treatment It is important to remember that people with mental illness are not criminals. They are in need of help and support. We need to create a criminal justice system that is more responsive to the needs of people with mental illness and less likely to punish them for their illness. Section IV: Predictive Learning Models in Mental Health Overview of existing learning models in predicting mental health needs Predictive learning models in mental health use machine learning algorithms to analyze data from various sources, such as electronic health records, prescription data, social media posts, and wearable devices, to identify individuals who are at risk for developing mental health problems or experiencing a mental health crisis. A variety of different learning models have been developed to predict mental health needs. Some of the most common types of models include: Logistic regression: Logistic regression is a statistical model that can be used to predict the probability of an event occurring, such as the probability of developing a mental health disorder. Logistic regression models are typically trained on data from large datasets of people with and without mental health disorders. Decision trees: Decision trees are another type of machine learning model that can be used for classification and prediction tasks. Decision tree models learn by recursively partitioning the data into smaller subsets based on the values of the input features. The model then predicts the outcome for each new data point based on the subset to which it belongs. Random forests: Random forests are an ensemble learning method that combines the predictions of multiple decision trees to produce a more accurate prediction. Random forest models are often used for complex classification and prediction tasks, such as predicting mental health needs. Support vector machines: Support vector machines (SVMs) are a type of machine learning model that can be used for classification and regression tasks. SVM models learn by finding a hyperplane that separates the data into two classes with as much margin as possible. The model then predicts the class of new data points based on their position relative to the hyperplane. Potential benefits and limitations of predictive learning models Predictive learning models have the potential to improve mental health care in several ways. For example, they can be used to: Identify individuals who are at risk for developing mental health problems, so that early intervention can be provided. Predict when individuals with existing mental health problems are at risk for a crisis, so that preventive measures can be taken. Personalize treatment plans for individuals with mental health problems, based on their individual risk factors and needs. Improve the efficiency of mental health care services by allocating resources to those who need them most. However, it is important to note that predictive learning models also have some limitations. For example, they are not always accurate, and they can be biased depending on the data they are trained on. Additionally, it is important to use predictive learning models in a responsible and ethical way, to ensure that they do not violate the privacy or autonomy of individuals. Case studies or examples of successful implementation A number of studies have shown that predictive learning models can be used to accurately predict mental health needs. For example, a 2022 study published in the journal Scientific Reports found that a machine learning model was able to predict with 85% accuracy whether or not adolescents were at risk for suicide or self-harm. Another example is the use of predictive learning models to identify children who are at risk for developing mental health problems. A 2019 study published in the journal Pediatrics found that a machine learning model was able to predict with 80% accuracy whether or not children would develop a mental health disorder within two years. These are just a few examples of how predictive learning models are being used to improve mental health care. Section V. Policy Recommendations Developing learning models based on prescription adjustment requests. One way to improve mental health care is to develop learning models based on prescription adjustment requests. These models could be used to identify individuals who are at risk for a mental health crisis or who may need a change in their medication regimen. Developing these models would require collecting data on prescription adjustment requests, as well as data on other relevant factors, such as the patient's medical history, demographics, and social support system. Integrating these models into the criminal justice system Another policy recommendation is to integrate these learning models into the criminal justice system. This could be done in several ways. For example, the models could be used to identify individuals who are at risk for recidivism or who may need mental health services while in custody. Additionally, the models could be used to develop diversion programs that would help individuals with mental health disorders avoid the criminal justice system altogether. Potential challenges and solutions in implementing these policies. There are several potential challenges in implementing these policy recommendations. One challenge is that collecting data on prescription adjustment requests would require the cooperation of healthcare providers and patients. Additionally, it is important to ensure that the learning models are developed and implemented in a fair and ethical manner. One way to address these challenges is to develop a pilot program to test the feasibility of using learning models to predict mental health needs in the criminal justice system. The pilot program could be conducted in a small number of jurisdictions and could involve a limited number of participants. The data collected from the pilot program could be used to refine the learning models and to develop best practices for implementing them. By identifying individuals who are at risk for a mental health crisis or who need a change in their medication regimen, these models could help to improve mental health outcomes and reduce recidivism. Section VI. Conclusion Recap of the issues and proposed solutions This paper has discussed the intersection of mental health and criminal justice, the limitations of the current approach to mental health treatment, and the potential of predictive learning models to improve mental health care and reduce criminal justice involvement. The current approach to mental health treatment has a number of limitations, including the complexity of mental health disorders, the shortage of mental health professionals, and the stigma and discrimination associated with mental illness. These limitations can make it difficult for people with mental health disorders to access timely and effective care. Predictive learning models can help to address these limitations by providing a more efficient and personalized approach to mental health care. These models can be used to identify individuals who are at risk for a mental health crisis, so that preventive interventions can be targeted. Additionally, predictive learning models can be used to optimize medication treatment plans and reduce side effects. The paper proposes developing predictive learning models based on prescription adjustment requests and integrating these models into the criminal justice system. This would allow for earlier identification and intervention for people with mental health disorders who are at risk of coming into contact with the criminal justice system. The potential impact on patients, healthcare providers, and the criminal justice system The development and implementation of predictive learning models in mental health care could have a significant impact on patients, healthcare providers, and the criminal justice system. • For patients, predictive learning models could lead to earlier identification and intervention for mental health disorders, which could improve outcomes and reduce the likelihood of a mental health crisis. Additionally, predictive learning models could help to optimize medication treatment plans and reduce side effects. • For healthcare providers, predictive learning models could provide a more efficient and personalized approach to mental health care. These models could help to identify individuals who are at risk for a mental health crisis, so that preventive interventions can be targeted. Additionally, predictive learning models could help to optimize medication treatment plans and reduce side effects. • For the criminal justice system, predictive learning models could help to reduce the number of people with mental health disorders who come into contact with the system. This could be done by identifying individuals who are at risk for a mental health crisis and providing them with preventive interventions. Additionally, predictive learning models could be used to develop more effective treatment programs for people with mental health disorders who are involved in the criminal justice system. Final thoughts and future directions The development and implementation of predictive learning models in mental health care is a promising new approach to improving care and reducing criminal justice involvement. However, there are several challenges that need to be addressed before these models can be widely implemented. One challenge is the need for more data. Predictive learning models are trained on data, so the more data that is available, the more accurate the models will be. However, there is currently a lack of data on mental health and criminal justice involvement. Another challenge is the need to develop models that are fair and equitable. Predictive learning models can be biased, so it is important to develop models that are fair to all individuals, regardless of race, ethnicity, socioeconomic status, or other factors. Finally, it is important to ensure that predictive learning models are used ethically and responsibly. These models should be used to inform clinical decision-making, not to replace the judgment of healthcare professionals. Additionally, it is important to protect the privacy of individuals whose data is used to train predictive learning models. For further reading and citation, I recommend these articles: (1) Supervised Medication Adjustment: What to Expect | Banner Health. (2) Inappropriate prescribing - American Psychological Association (APA). (3) Criminalization of People with Mental Illness - NAMI. (4) Mental Illness and the Justice System - The Crime Report. (5) How the Criminal Justice System Fails People With Mental Illness. (6) An AI-based Decision Support System for Predicting Mental Health .... (7) Machine Learning in Mental Health: A Systematic Review of the HCI .... (8) Mental Health Prediction Using Machine Learning: Taxonomy ... - Hindawi (9) Researchers design machine learning models to better predict adolescent .... (10) Behavioral Modeling for Mental Health using Machine Learning ... - Springer (11) Psychopharmacology guide on prescribing psychotropic medications .... (12) Paying for Your Medication | NAMI: National Alliance on Mental Illness (13) Understanding Medicare Coverage for Mental Health - Verywell Mind (14) Addressing Mental Health and the Criminal Justice System (15) Articles of Courts Literature Review - California Courts
How to purchase a Congressperson for Peanuts September 28, 2023 DeSantis in Decline: President Donald Trump, and the decline of traditional Republicanism September 21, 2023 New Way to Prevent Criminal Justice Involvement for People with Mental Illness September 18, 2023
How to purchase a Congressperson for Peanuts September 28, 2023 Top Menu “Like all Americans, I am sick and tired of the corruption, and I want it to be exposed.” Congressperson Kat Cammack In 2023, the Peanut industry emerged as one of the most heavily subsidized sectors in the United States. The subsidy program, providing over $800 million annually, benefits approximately 6,500 "Farms." These businesses, typically affluent financially secure "Farms" are concentrated among a relatively small number of wealthy individuals or corporations. The peanut shelling industry is dominated by two powerful companies that together buy 80 percent of all peanuts grown in the U.S. This monopoly power controls pricing in the industry. Subsidizing the peanut industry cost U.S. taxpayers more than $2 billion from 2014 through 2018. It is the most costly per-acre crop to taxpayers in America, in large part because monopoly power controls pricing in the industry. This can distort the market, creating an uneven playing field that makes it challenging for family farms to compete. Even worse during hard times, smaller family farms often struggle to stay afloat without the financial support of Congress these Corporations are receiving, and as a result, they fail and are bought up by larger corporations, with taxpayers money. This has created the consolidation of the agricultural industry and worsens the family farms struggle by the redistributing of wealth from taxpayers money to large corporations. It makes it difficult, if not impossible, for smaller family farms to compete. As large Corporations buy up smaller family farms, the number of independent farms declines. This reduces competition and makes it more difficult for family farms to get a fair price for their peanuts. This is nothing more than a redistributing of wealth from Congress to large corporations which means that taxpayer money is not being used to support small family farms but Corporations. Congressperson Kat Cammack has been a strong advocate for the Peanut Subsidies. “In #FL03, we are all about peanuts.” Congressperson Kat Cammack According to the USDA, the number of farms in the U.S. declined by 3% between 2012 and 2017, while the average farm size increased by 2%. The share of farms operated by corporations rose from 1.6% in 2012 to 1.8% in 2017, while the share of farms operated by families fell from 97% to 96%. Despite this shift, family farms still make up a significant portion of U.S. agriculture, accounting for 86% of farm production in 2017. However, the largest 15 percent of farm businesses receive over 85 percent of farm subsidies, and just ten percent of America's largest and richest farms collect almost three-fourths of federal farm subsidies. This indicates that a significant portion of agricultural subsidies is going to large and corporate farms, even though most of the agriculture is produced by small family farms. Congressperson Kat Cammack has been a strong advocate for the peanut industry and its interests. She has supported the Farm Bill, which provides funding for various agricultural programs, including peanut subsidies. She has also opposed foreign peanut subsidies that she claims harm domestic producers and consumers. She has stated that "food security is national security," highlighting the critical role of agriculture in maintaining the nation's wellbeing. But these subsidies are unfair to taxpayers, we are essentially paying for large corporations to get even richer. Peanut subsidies distort the market, making it difficult for smaller family farms to compete. Peanut subsidies are not even necessary to protect the peanut industry, as peanuts are a relatively profitable crop. Then why is Congressperson Kat Cammack a strong advocate for the Peanut Subsidies? “We must work towards draining the self-serving bureaucracy out of the Washington swamp." Congressperson Kat Cammack The peanut subsidy program makes it difficult, if not impossible, for smaller family farms to compete. The subsidies allow larger farms to produce peanuts at a lower cost, which gives them an unfair advantage in the marketplace. The peanut programs focus on large farms contributes to the corporate consolidation of American agriculture. In other words, the peanut subsidy program is a way for wealthy individuals and corporations to purchase politicians with taxpayer money. They donate to the campaigns of politicians who will support the subsidy program, and in return, they receive billions of dollars in subsidies each year. Further Reading The Peanut Program: An Unwarranted Windfall for Wealthy Farmers, a report by the American Enterprise Institute that analyzes the costs and benefits of the peanut subsidy program and its impact on the market and consumers. Peanut Subsidies in the United States, a report by the Environmental Working Group that provides data on the distribution and recipients of peanut subsidies from 1995 to 2020. The Farm Bill: A Citizen's Guide, a book by Daniel Imhoff and Christina Badaracco that explains the history, politics, and economics of the Farm Bill and its implications for food, farming, and the environment.
DeSantis in Decline: President Donald Trump, and the decline of traditional Republicanism September 21, 2023 Top Menu Since 2016, the Republican Party has shifted significantly to the right, with most Republicans now supporting populist, nationalist, and anti-establishment views. Governor Ron DeSantis of Florida is widely seen as a potential successor to Trump. DeSantis has been a close ally of Trump, endorsing him in 2016 and 2020, and adopting many of his policies and positions on issues such as immigration, health care, education, and the coronavirus pandemic. DeSantis has also gained popularity among Republican voters for his handling of the pandemic, which has been praised by some for being pro-business and pro-freedom and criticized by others for being reckless and irresponsible. However, DeSantis's approval rating among Republican voters has declined in recent months, according to a Harvard CAPS / Harris Poll conducted in May 2023. The poll found that only 52% of Republican voters approve of the job DeSantis is doing as governor, down from 63% in January 2023. On March 26, 2023, former president Donald Trump delivered a fiery speech at a rally in Texas, where he attacked his former ally and potential rival, Florida governor Ron DeSantis. Trump accused DeSantis of being weak on immigration, crime, and COVID-19, and mocked his weight and appearance. The speech was widely seen as a sign of Trump's intention to run for president again in 2024, and a challenge to DeSantis. The event marked a turning point in the Republican primary race, and the beginning of a bitter feud between the two men. DeSantis's popularity has taken a hit since March 26, when he also signed a controversial bill that restricted voting rights in Florida. The poll shows that his net favorability rating among all voters dropped from +11 to -4, a 15-point swing in less than a month. A recent Harvard CAPS / Harris Poll found that the top issues of concern for American voters are the economy, health care, and climate change. All three of these issues are areas where DeSantis has been criticized for his record. According to the poll, 59% of voters say that curbing inflation is a top priority, followed by balancing the budget/tightening government spending (57%), encouraging domestic oil and gas exploration (60%), reducing China tariffs (58%), and lowering interest rates (57%). The same poll found that DeSantis received less than 10% confidence among Republican voters in these areas. Being these are all areas of particular interest to Republican voters, suggesting that DeSantis's record on these issues could be a liability for him in the future. It remains to be seen whether DeSantis can overcome his recent decline in popularity and become the Republican nominee for president in 2024. However, it is clear that he faces significant challenges, both from Trump and from other Republicans who are seeking the nomination. All data used to evaluate Governor Ron DeSantis can be found here Harvard CAPS / Harris Poll Please feel free to download, and share any insights you may have.
A New Way to Prevent Criminal Justice Involvement for People with Mental Illness: Using Prescription Data to Predict Mental Health Needs September 18, 2023 Top Menu Mental illness is a major public health problem, with one in five adults in the United States experiencing a mental illness each year. People with mental illness are also more likely to come into contact with the criminal justice system than those without mental illness. In fact, one-third of all jail inmates have a serious mental illness. One way to reduce criminal justice involvement for people with mental illness is to develop predictive models that can identify those who are at risk for a mental health crisis. These models could be used to target preventive interventions, such as increased access to mental health care or support groups. **Thesis statement:** This paper proposes a new policy recommendation for using prescription data to predict mental health needs and reduce criminal justice involvement for people with mental illness. This paper is divided into six sections: Section II: The Current State of Mental Health Treatment This section provides an overview of current methods in mental health treatment, the role of prescription adjustments in mental health treatment, and the limitations and challenges of the current approach. Section III: The Intersection of Mental Health and Criminal Justice This section discusses the criminalization of mental illness, provides case studies of mental illness treatment in the criminal justice system, and argues for the need for a mental health response rather than a law enforcement response. Section IV: Predictive Learning Models in Mental Health This section provides an overview of existing learning models in predicting mental health needs, discusses the potential benefits and limitations of these models, and provides case studies or examples of successful implementation. Section V: Policy Recommendations This section proposes developing learning models based on prescription adjustment requests and integrating these models into the criminal justice system. It also discusses potential challenges and solutions in implementing these policies. Section VI: Conclusion This section recaps the issues discussed in the paper and the proposed solutions. It also considers the potential impact on patients, healthcare providers, and the criminal justice system, and offers final thoughts and future directions. In addition to the six main sections, the paper also includes a list of references for further reading and citation.
Section II: The Current State of Mental Health Treatment The current state of mental health treatment is complex and multifaceted. There is no one-size-fits-all approach, and the best treatment plan for any individual will vary depending on their specific needs. However, some of the most common methods of mental health treatment include: Psychotherapy: Psychotherapy, also known as talk therapy, is a type of treatment that involves talking to a mental health professional about your thoughts, feelings, and experiences. Psychotherapy can help you to understand your mental health condition better, develop coping mechanisms, and make positive changes in your life. Medication: Medication can be a very effective way to treat many mental health conditions. However, it is important to note that medication should not be used alone and should always be used in conjunction with psychotherapy. Lifestyle changes: Lifestyle changes, such as getting regular exercise, eating a healthy diet, and getting enough sleep, can also play a role in improving mental health. The role of prescription adjustments in mental health treatment Prescription adjustments are a common part of mental health treatment. Sometimes, people with mental health conditions need to have their medication dosage adjusted to achieve the best possible results. Prescription adjustments can also be necessary if a person is experiencing side effects from their medication. It is important to note that prescription adjustments should only be made by a qualified mental health professional. There is no one-size-fits-all approach to prescription adjustments, and the best way to determine the right dosage for any individual will vary depending on their specific needs. Limitations and challenges in the current approach The current approach to mental health treatment has several limitations and challenges. One challenge is that there is a shortage of mental health professionals, which can make it difficult for people to get access to the care they need. Additionally, mental health care can be expensive, and not everyone has access to affordable coverage. Another challenge is that the stigma associated with mental illness can prevent people from seeking help. People with mental illness may be afraid of being judged or discriminated against, which can lead to them delaying or avoiding treatment. Finally, it is important to note that mental health conditions are complex, and there is no one-size-fits-all treatment approach. This can make it difficult to find the right treatment for any individual, and it may take some time and experimentation to find what works best. Despite these challenges, there has been significant progress in the field of mental health treatment in recent years. Researchers are developing new and more effective treatments, and there is a growing awareness of the importance of mental health care. With continued progress, we can hope to create a future where everyone has access to the care, they need to live a healthy and fulfilling life.
Section III: The Intersection of Mental Health and Criminal Justice Mental illness is a major public health problem, but it is also a criminal justice problem. People with mental illness are more likely to be arrested, incarcerated, and subjected to police brutality than people without mental illness. This is due to a number of factors, including: Lack of access to mental health care Misunderstanding and stigma surrounding mental illness Law enforcement officers' lack of training in dealing with people with mental illness Case studies of mental illness treatment in the criminal justice system There are many cases of people with mental illness being mistreated in the criminal justice system. For example: In 2016, Eric Garner, a man with schizophrenia, was killed by police in Staten Island, New York, after he was placed in a chokehold. Garner had been repeatedly telling officers that he could not breathe, but they ignored him. In 2017, Tanisha Anderson, a woman with bipolar disorder, was killed by police in Cleveland, Ohio, after they were called to her apartment because she was having a mental health crisis. Anderson was unarmed and was shot multiple times by police. In 2020, Daniel Prude, a man with schizophrenia, was killed by police in Rochester, New York, after he was placed in a mesh hood and hogtied. Prude died of asphyxiation. These are just a few examples of the many cases of people with mental illness being mistreated and killed by police. The need for a mental health response rather than a law enforcement response When people with mental illness are in crisis, they need a mental health response, not a law enforcement response. Law enforcement officers are not trained to deal with people with mental illness, and their interactions with people with mental illness often lead to violence and death. There are a number of things that can be done to reduce the criminalization of mental illness and improve the treatment of people with mental illness in the criminal justice system. These include: Increasing access to mental health care Educating law enforcement officers about mental illness Establishing mental health courts Diverting people with mental illness away from the criminal justice system and into mental health treatment It is important to remember that people with mental illness are not criminals. They are in need of help and support. We need to create a criminal justice system that is more responsive to the needs of people with mental illness and less likely to punish them for their illness.
Section IV: Predictive Learning Models in Mental Health Overview of existing learning models in predicting mental health needs Predictive learning models in mental health use machine learning algorithms to analyze data from various sources, such as electronic health records, prescription data, social media posts, and wearable devices, to identify individuals who are at risk for developing mental health problems or experiencing a mental health crisis. A variety of different learning models have been developed to predict mental health needs. Some of the most common types of models include: Logistic regression: Logistic regression is a statistical model that can be used to predict the probability of an event occurring, such as the probability of developing a mental health disorder. Logistic regression models are typically trained on data from large datasets of people with and without mental health disorders. Decision trees: Decision trees are another type of machine learning model that can be used for classification and prediction tasks. Decision tree models learn by recursively partitioning the data into smaller subsets based on the values of the input features. The model then predicts the outcome for each new data point based on the subset to which it belongs. Random forests: Random forests are an ensemble learning method that combines the predictions of multiple decision trees to produce a more accurate prediction. Random forest models are often used for complex classification and prediction tasks, such as predicting mental health needs. Support vector machines: Support vector machines (SVMs) are a type of machine learning model that can be used for classification and regression tasks. SVM models learn by finding a hyperplane that separates the data into two classes with as much margin as possible. The model then predicts the class of new data points based on their position relative to the hyperplane. Potential benefits and limitations of predictive learning models Predictive learning models have the potential to improve mental health care in several ways. For example, they can be used to: Identify individuals who are at risk for developing mental health problems, so that early intervention can be provided. Predict when individuals with existing mental health problems are at risk for a crisis, so that preventive measures can be taken. Personalize treatment plans for individuals with mental health problems, based on their individual risk factors and needs. Improve the efficiency of mental health care services by allocating resources to those who need them most. However, it is important to note that predictive learning models also have some limitations. For example, they are not always accurate, and they can be biased depending on the data they are trained on. Additionally, it is important to use predictive learning models in a responsible and ethical way, to ensure that they do not violate the privacy or autonomy of individuals. Case studies or examples of successful implementation A number of studies have shown that predictive learning models can be used to accurately predict mental health needs. For example, a 2022 study published in the journal Scientific Reports found that a machine learning model was able to predict with 85% accuracy whether or not adolescents were at risk for suicide or self-harm. Another example is the use of predictive learning models to identify children who are at risk for developing mental health problems. A 2019 study published in the journal Pediatrics found that a machine learning model was able to predict with 80% accuracy whether or not children would develop a mental health disorder within two years. These are just a few examples of how predictive learning models are being used to improve mental health care.
Section V. Policy Recommendations Developing learning models based on prescription adjustment requests. One way to improve mental health care is to develop learning models based on prescription adjustment requests. These models could be used to identify individuals who are at risk for a mental health crisis or who may need a change in their medication regimen. Developing these models would require collecting data on prescription adjustment requests, as well as data on other relevant factors, such as the patient's medical history, demographics, and social support system. Integrating these models into the criminal justice system Another policy recommendation is to integrate these learning models into the criminal justice system. This could be done in several ways. For example, the models could be used to identify individuals who are at risk for recidivism or who may need mental health services while in custody. Additionally, the models could be used to develop diversion programs that would help individuals with mental health disorders avoid the criminal justice system altogether. Potential challenges and solutions in implementing these policies. There are several potential challenges in implementing these policy recommendations. One challenge is that collecting data on prescription adjustment requests would require the cooperation of healthcare providers and patients. Additionally, it is important to ensure that the learning models are developed and implemented in a fair and ethical manner. One way to address these challenges is to develop a pilot program to test the feasibility of using learning models to predict mental health needs in the criminal justice system. The pilot program could be conducted in a small number of jurisdictions and could involve a limited number of participants. The data collected from the pilot program could be used to refine the learning models and to develop best practices for implementing them. By identifying individuals who are at risk for a mental health crisis or who need a change in their medication regimen, these models could help to improve mental health outcomes and reduce recidivism.
Section VI. Conclusion Recap of the issues and proposed solutions This paper has discussed the intersection of mental health and criminal justice, the limitations of the current approach to mental health treatment, and the potential of predictive learning models to improve mental health care and reduce criminal justice involvement. The current approach to mental health treatment has a number of limitations, including the complexity of mental health disorders, the shortage of mental health professionals, and the stigma and discrimination associated with mental illness. These limitations can make it difficult for people with mental health disorders to access timely and effective care. Predictive learning models can help to address these limitations by providing a more efficient and personalized approach to mental health care. These models can be used to identify individuals who are at risk for a mental health crisis, so that preventive interventions can be targeted. Additionally, predictive learning models can be used to optimize medication treatment plans and reduce side effects. The paper proposes developing predictive learning models based on prescription adjustment requests and integrating these models into the criminal justice system. This would allow for earlier identification and intervention for people with mental health disorders who are at risk of coming into contact with the criminal justice system. The potential impact on patients, healthcare providers, and the criminal justice system The development and implementation of predictive learning models in mental health care could have a significant impact on patients, healthcare providers, and the criminal justice system. • For patients, predictive learning models could lead to earlier identification and intervention for mental health disorders, which could improve outcomes and reduce the likelihood of a mental health crisis. Additionally, predictive learning models could help to optimize medication treatment plans and reduce side effects. • For healthcare providers, predictive learning models could provide a more efficient and personalized approach to mental health care. These models could help to identify individuals who are at risk for a mental health crisis, so that preventive interventions can be targeted. Additionally, predictive learning models could help to optimize medication treatment plans and reduce side effects. • For the criminal justice system, predictive learning models could help to reduce the number of people with mental health disorders who come into contact with the system. This could be done by identifying individuals who are at risk for a mental health crisis and providing them with preventive interventions. Additionally, predictive learning models could be used to develop more effective treatment programs for people with mental health disorders who are involved in the criminal justice system. Final thoughts and future directions The development and implementation of predictive learning models in mental health care is a promising new approach to improving care and reducing criminal justice involvement. However, there are several challenges that need to be addressed before these models can be widely implemented. One challenge is the need for more data. Predictive learning models are trained on data, so the more data that is available, the more accurate the models will be. However, there is currently a lack of data on mental health and criminal justice involvement. Another challenge is the need to develop models that are fair and equitable. Predictive learning models can be biased, so it is important to develop models that are fair to all individuals, regardless of race, ethnicity, socioeconomic status, or other factors. Finally, it is important to ensure that predictive learning models are used ethically and responsibly. These models should be used to inform clinical decision-making, not to replace the judgment of healthcare professionals. Additionally, it is important to protect the privacy of individuals whose data is used to train predictive learning models. For further reading and citation, I recommend these articles: (1) Supervised Medication Adjustment: What to Expect | Banner Health. (2) Inappropriate prescribing - American Psychological Association (APA). (3) Criminalization of People with Mental Illness - NAMI. (4) Mental Illness and the Justice System - The Crime Report. (5) How the Criminal Justice System Fails People With Mental Illness. (6) An AI-based Decision Support System for Predicting Mental Health .... (7) Machine Learning in Mental Health: A Systematic Review of the HCI .... (8) Mental Health Prediction Using Machine Learning: Taxonomy ... - Hindawi (9) Researchers design machine learning models to better predict adolescent .... (10) Behavioral Modeling for Mental Health using Machine Learning ... - Springer (11) Psychopharmacology guide on prescribing psychotropic medications .... (12) Paying for Your Medication | NAMI: National Alliance on Mental Illness (13) Understanding Medicare Coverage for Mental Health - Verywell Mind (14) Addressing Mental Health and the Criminal Justice System (15) Articles of Courts Literature Review - California Courts
