Data And Decision Analytics Assessment

Differentiating Data Types

The main goal of income statement data is to look at the company’s performance. This helps people make smart choices for their business plans. Profit margin, a key measure in money numbers, shows how well businesses turn income into profit. For example, a big gross profit margin might mean good cost control. On the other hand, falling net profit margins might hint at possible problems in operations running or setting prices correctly. Money numbers help make choices about using resources, planning for buying stocks, and savings measures. By knowing money info, groups can make good choices that help with long-term cash stability (Mikalef et al.,2019). This kind of data gives valuable information about how an organization works daily. It can be used to check whether internal processes are working well. Examples of operation data include things made in production, measures for service delivery, and signs of how well the supply chain functions. Operations data helps improve processes, increase work output, and maintain company efficiency. A big number, cycle time, counts how long a certain job takes. Making things faster in factories makes them work better and deliver on time. This information backs up choices on improving things, sharing resources, and finding what is slowing everything down. Looking at work data helps make smart choices for smoother processes and better productivity.

Customer data helps measure and improve customer satisfaction, loyalty, and overall experience. This is done by looking at customer feedback and something called Net Promoter Scores (NPS), which also includes basic information about people, such as their age or where they live. The main goal of getting customer information is to see how well a business meets their needs and find ways to improve things. A big part of customer information is the Net Promoter Score. It tells how likely people are to suggest a company’s goods or services to others. For example, good NPS scores from an online store mean customers are happy and loyal. Information about customers is essential for making choices on developing products, marketing plans, and managing customer relationships.

Companies can make smart choices that improve happiness and grow lasting ties by carefully studying customer information. Imagine a picture combining money and business information with customer info to show the difference between these data sources. A bar chart with many layers can show how money from four parts of the year grows, where each bit shows income for different kinds of products (money details), the time it took to make every product and the happiness score that people have when they buy stuff during those quarters. These images help you understand quickly how things are going overall and find places to make them better in each part. It helps bosses see big pictures of data that’s hard to grasp and guide planning based on a full view of the organization’s performance. These data types are connected and essential for businesses that want to understand how they’re doing entirely. Data about money, work, and customers is vital to making smart choices. This helps companies to succeed. Combining and studying different data sources is necessary for dealing with difficulties, grabbing chances, and making a strong path.

Data Visualization Graphic

Data Visualization Graphic

Typically, looking at the fast expansion and money skills, it is essential to see how amazing it was for Amazon to reach $1 trillion. Amazon, a big player in shopping online, always does better than others. It has left companies like Alibaba way behind. 2017, the company had a substantial net income of $177 million. This amount was much more than about $23 million made by its Asian competitor Alibaba. This money power was not just a one-time thing. It led to an even more amazing path for growth in the future. Bloomberg Intelligence Analysis, known for its smart guesses on the future, thinks that Amazon’s total money will jump to $330 billion. This prediction shows that the company’s growth has been consistent and strong. It is driven by its necessary plans and control over markets. Analysts found that one major cause is the world growth of Amazon Prime.

Furthermore, this move will help strengthen its place in all countries worldwide. As Amazon keeps making new and different items, the chance of getting a trillion dollars in total money by 2025 seems close. This big thing Amazon is about to do means more than just a symbol. It shows how successful and scary it has become for traditional stores trying hard to stay caught up. Jeff Bezos’ huge retail business, which has grown to one trillion dollars, shows how it makes money. Also, it causes worries for other businesses trying hard to match the changing world of shopping today. Amazon’s big operations and ability to catch trends make it the best in business. A picture showing data shows its fast-growing money, which is very different from others. The trip to a one trillion-dollar worth offers Amazon’s innovative thinking and new ideas and cannot be beaten power in internet shopping. It marks a vital money goal reached.

Benefits of Data Analysis Methods

Businesses use many ways to find important information in the busy world of studying data. They use two big methods: looking at what happened (descriptive analysis) and guessing future results (predictive analysis). A descriptive analysis method looks at main numbers to show how well things are doing. For example, consider a case where a store uses descriptive analysis to make reports every three months. The group looks at how it does by boiling money, business, and people information. Description study helps to see trends, patterns, and places for betterment. This way is usually used often, like every three months or once a year. It gives people who care about the company an idea of how it is doing at any given time. The good thing about descriptive analysis is that it can turn big data into simple ideas. This makes decision-making easier at different levels, like daily work and long-term planning. However, predictive analysis means guessing what will happen in the future using past information. This lets businesses prepare for possible situations before they even occur. To show how predictive analysis works, think about a factory that uses this method to guess what people will want. By looking at past sales records, up-to-date market changes, and things happening outside the business, it can guess how many people might want to buy its items in the future. This helps make smart choices about keeping track of stock, organizing production schedules, and deciding how to use resources (Ayala-Chauvin et al.,2023). Predictive analysis is constructive in jobs where demand changes a lot. It’s often used to manage supply chains. The good things about using predictive analysis are better planning, reducing dangers, and the chance to take advantage of new opportunities.

Both ways of analyzing data and describing and predicting results have unique benefits for a business. Descriptive analysis looks back at what has happened. It helps organizations see how they did in the past and make decisions based on older information. It’s beneficial for setting standards, finding exceptions, and creating a comparison base. However, predictive analysis brings a future-looking aspect to making decisions. By guessing what will happen and the possible results, businesses can plan to keep ahead of rivals. They also know how best to handle doubts or changes in the future. These techniques have been used in many different businesses in real-life business situations. For example, a big shopping company might use describing ways to check how well its worldwide stores are doing. The business learns about differences by looking at how much is sold, working well, and making customers happy in different areas. This helps them change their plans to match what folks like best there. In another case, a technology company that makes software may use predictive analysis to guess how many people will start using its new app.

The company can guess how much they will need in the future by looking at past use, what people say about it, and market changes. This way, they can arrange things effectively to meet those needs better. The decision between using descriptive or predictive analysis comes from your business issue. Descriptive analysis is best when we need a history of events, like looking at past results or finding areas to improve from old information. Predictive analysis is suitable when companies want to guess what will happen, think about possible future problems, and use new chances. For example, in planning for money management, a descriptive study might help measure how effective old-budget spending was. At the same time, forecasting or predictive analysis could guide decisions about future resource use by foreseeing possible market changes.

Justifying Strategic Choice

Making a decision based on looking at data is very important when you are deciding something. It aids us in creating smart and strong outcomes. A good example is using regression analysis to help make a big decision in marketing. Picture a case where a large internet shopping firm is considering altering its advertising method. Regression analysis can help a lot in finding out how much ad cost impacts sales growth. We should choose the best way to spend our ad money so we get big benefits from it. By checking past information with regression analysis, the team can use numbers to see how much money spent on ads changes sales. The method involves finding the important things like how much we spend on ads and growth in sales. We need to gather information from different times, too. Then, the regression model looks at the data. It gives numbers showing how strong and how a connection between variables is going on. A good number means a positive connection, indicating that more ad spending leads to higher sales. Using this number information, those in charge can explain why they should keep or raise their advertising budget to push sales growth.

This decision based on regression analysis is critical in fast-changing market situations where companies have limited money for advertising. They want to use their resources best so they get the biggest results. For instance, when introducing a new product or running advertisements, we can use regression analysis to help decide our spending on adverts so that we reach sales goals. The main reason is that regression analysis can help us know how marketing work affects sales. This allows for decisions made based on facts and data instead of guesswork. Another way we can prove a smart decision supported by data is by using product grouping through calculation in business retail. Here, the big choice is about making marketing plans just for certain groups of customers. This helps make everything work better in total. Clustering analysis helps an organization put customers into groups based on the same shopping habits, likes, and information about where they live. This shows different parts within their list of buyers (Saunders et al. 2019). The method includes getting and studying customer information, finding patterns, and grouping customers with the same qualities. These groups are parts of the customer group that have similar features. After groups are made, the company can show why its big plan is good by making sales campaigns to connect with what each part likes and needs. For example, if a group mostly has people who watch their money spending closely, then the marketing plan might focus on sale offers, price reductions, and highlights that show good value.

The reason to use clustering analysis is because it can find hidden customer groups that might be missed if we don’t look. When businesses focus their marketing on certain groups, they can make customers more interested, get higher conversion rates, and use money spent on marketing best. This smart decision matches the bigger aim of making marketing actions as effective as possible by uniquely dealing with special requirements for different customer groups. In real-life business situations, companies in various fields have made smart decisions using different ways of looking at data. For example, a big tech firm could use social media feeling study to help decide how they make products. When a company looks at what people like or dislike, they learn about their customers’ tastes. This helps them decide if tweaking old products or building new ones that meet the needs of those customers better is needed. Data analysis techniques are not only used to make marketing-related choices. In business management, companies could use time series analysis to improve how much inventory they have. The group can make smart choices about managing stock by looking at past sales info and guessing future demand. This makes sure products are there when customers want them.

Big Data’s Influence on Organizational Performance

Big data has changed how companies work. It gives them new chances to make decisions based on information. Big data can change how an organization operates in many areas because of its vast amount, quick speed, and different types. A good example of how big data affects us is through analyzing social media. Big data is crucial in understanding what people think, like, and do on social media. Imagine a big online shopping company that uses smart data tools to watch social media websites. The group can find out directly from people what they think of their products, how well marketing works, and what new things are happening in the market by looking at lots of stuff made by users. Analyzing feelings is a part of studying social media use that lets companies understand what people think about their brand. By figuring out the feelings and settings of social media talk about them, businesses can look at how people generally feel – good or bad. (Guru, 2023) For example, if a new product launch gets positive feedback on social media, the group can use that info to improve their marketing and take advantage of all the good talk.

Extensive data analysis helps companies find connections and patterns that might not be seen at first by using old-fashioned ways of looking at information. In our social media check-up example, smart computer programs could find hidden links between customer details like age, place of living, and buying choices. Big data analysis gives companies the power to adjust their products and services. This allows them to match what customers want as it changes over time. Big data affects how well an organization does, and it is about more than just marketing and customer relations. In managing supplies, big data analysis can improve items’ stock levels, improve logistics, and cut down costs in operations. Predictive analytics can guess how much people want. This lets companies plan their supply chain for future market trends. But, using a lot of data can be challenging. There are worries about privacy and safety, along with the need for special skills in understanding numbers well. Businesses must handle these problems to use big data and improve their performance. Even though there are some problems, big data dramatically affects how well organizations work. It gives unique knowledge and advantages for making critical future decisions.

Conclusion

Checking how well an organization is doing includes different kinds of data. They relate to its money, how well it works, and what customers think about it. These types of data help with big decisions. Presenting the information is very important to change raw numbers into understandable ideas that people can use. Tools that help us see things, like the money growth chart every three months, make it easy for people who care to understand big changes quickly. Data is a way of looking at information, like explaining past events, guessing future ones, showing what happened before, and talking about the upcoming stuff. This gives a good base for making plans in advance. Big data are changing organizational assessment with its large datasets and complex analysis. For example, social media analysis gives immediate information that helps shape marketing plans and product creation. But, joining big data brings problems like privacy and the need for better skills in numbers. Organizations need to think carefully about these problems if they want to use big data in a way that helps drive performance growth. In this age of making data-based decisions, it is essential to be flexible and creative for a great company. Different kinds of data, ways to show information, and methods for looking at it all together with lots of big data make a complete plan. This helps guide you through uncertainties while grabbing good chances in life or work. The future of how companies check their performance comes from always improving the use of data. This will help businesses change all the time in a world that never stops changing.

References

Ayala-Chauvin, M., Avilés-Castillo, F., & Buele, J. (2023). Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador. Computers, 12(7), 146. https://www.mdpi.com/2073-431X/12/7/146

Guru, S. K. (2023). Influence of Big Data Analytics on Business Intelligence. In Analytics Enabled Decision Making (pp. 45-58). Singapore: Springer Nature Singapore. https://elearningindustry.com/the-impact-of-big-data-analytics-on-business-decision-making#:~:text=SummaryBigDataanalyticshas,aheadinthecompetitivemarket.

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. https://doi.org/10.1016/j.jbusres.2019.01.044

Saunders, M., Lewis, P., Thornhill, A., & Bristow, A. (2019, March 2). “Research Methods for Business Students” Chapter 4: Understanding research philosophy and approaches to theory development. ResearchGate.https://www.researchgate.net/publication/330760964_Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development

Data Science Skills Enhancement

Section 1: Job Role

Job Role

Section 2: Skills Required

A Data Scientist’s job at the Science and Technology Facilities Council (STFC) needs special technology-related skills. These skills make them suitable for their work. These skills are essential for doing my job well. They also help me to be successful in my role. The technical or specialized skills needed by the position require a deep knowledge of data analysis and statistical modelling methods. A good data scientist should know how to use coding languages like Python or R to work with and examine data. I need to know a lot about machine learning methods, data picture tools, and working with extensive information systems if I want this job.

Communication

Effective communication is essential for Data Scientists. It helps connect complicated data discoveries and people who matter in a way everyone can understand. A good Data Scientist is not just good at explaining technical stuff to people who need to be more techy. They also work well with teams from different jobs or functions. This skill goes beyond just passing on information; it brings everyone together and ensures the value of data analysis is clear and adequately shared (Patel, 2023). Being able to explain hard facts quickly helps make data findings more powerful. This makes them useful for people who make choices and gives them valuable information. Communication is essential. It changes raw information into helpful stories, helping people make wise choices in every part of an organization.

Critical Thinking

Thinking critically is very important for data scientists because it helps them examine and understand data reasonably. I need to look at different data types, find patterns and make intelligent decisions based on facts. Using critical thinking, Data Scientists make a strong foundation for reliable data insights. A careful and fair way to study data makes the results correct. This helps people who do this job deal with difficulties and find important information from that data (Discover Data Science, 2023). This skill is not just a thought tool; it is the main thing that ensures analytical data science results are reliable and trustworthy.

Problem-Solving

Solving problems is very important for a data scientist. It means trying to fix complex issues using good ways of looking at data. Data Scientists need to find problems, make guesses about them and create solutions that use data. This skill is essential for their work in solving issues. This power helps them deal with big problems and improves their ability to learn valuable information from data. With their problem-solving ability, Data Scientists add significant value in helping organizations make decisions (Tableau, 2023). The careful way they solve problems and use data methods makes them very important. They can find helpful hints from complicated sets of numbers.

Adaptability

In the ever-changing world of data and tech, adaptability is essential for Data Scientists. The job needs adjusting to new tools and ways, quickly changing with trends, and smoothly adding new tech into the process of analysis workflow. A good Data Scientist does well when tech changes happen, showing they can be flexible and quick to react. This is very important. It helps people in this job stay up-to-date with the fast changes in their field and improve at making reports that matter greatly (Tableau, 2023). A good Data Scientist can handle changes and learn new ways. This makes them robust and creative people in the constantly changing field of data science.

Attention to Detail

In the intricate realm of data analysis, the essence lies in the details, making attention to detail a paramount skill for Data Scientists. In the tricky world of looking at data, it is all about paying attention to small things. This skill is essential for people who work with Data or are known as Data Scientists. This skill requires people to be very careful when checking data, ensuring results are correct and spotting anything strange. Data Scientists use a careful method, which is very important. This makes sure the results are correct and trustworthy. Paying close attention to details is very important. It helps avoid mistakes and gives me trust in the results of my analysis (Discover Data Science, 2023). Data Scientists carefully look at small details to find insights, keep the quality of their work high and help make accurate data-based decisions.

Independent Thinking

Working together is essential, but thinking alone makes a Data Scientist’s way of solving problems special. Independent thinking is the skill to create personal understandings, question ideas, and share unique viewpoints in the data-studying process. This skill improves the thinking process, helping us be more creative and find new ways of solving problems that are not usual. Thinking independently makes a Data Scientist’s contribution to teamwork more profound.

Section 3: Reflect On My Skills

Communication

In terms of communication, I have built a good base by doing different school-related and fun activities. While I was in college, I played a big part in team activities. This helped me improve at talking about tough science stuff to different people. I had to talk well with customers in my part-time customer service job. This made me better at explaining things clearly. I am good at this skill, but there is always a way to improve. To improve my talking skills, I want to attend public speaking classes and join groups or clubs promoting good communication (Patel, 2023). In my school, especially when doing presentations and working together on projects, I expect many chances to improve and use my talking skills.

Critical Thinking

Thinking critically has always been with me on my learning path. Through hard school work, I have learned to look at and study data somewhat, seeing patterns that help me make good choices. Working on research projects has helped me develop this skill. It lets me explore complicated problems and make important discoveries. We always need to improve our thinking skills. They are essential, but we must keep working on them (Discover Data Science, 2023). Taking part in case study contests and doing work that needs complex thinking will be my plan for getting better. As I move forward with my studies, especially in complicated data analysis classes, I see chances to improve my ability to think carefully.

Problem-Solving

My experience in both academic and professional settings has provided ample opportunities to exercise problem-solving skills. Working on different things in school has helped me learn to see problems, make guesses and develop data-based solutions. Also, my job in data analysis allowed me to use solution methods for real-life problems. I am good, but growth never stops. To improve my problem-solving skills, I want to join hackathons and get advice from people who are really good at their jobs (Tableau, 2023). The following parts of my class, especially new ways to solve challenging problems, will help me improve.

Adaptability

Adaptability has been essential to my studies and work. Going through different tasks and jobs has made me comfortable changing to new trends and technologies. Using different tools and ways during studying classes has made me better at changing. However, knowing how fast this area changes, I want to improve my ability even more. Participating in workshops on new tech and knowing what is happening will be necessary for my growth (Tableau, 2023). In the class, using new tools for data analysis will be a critical time to get better at being flexible.

Attention to Detail

I have always paid close attention to detail in my schoolwork. Complex study, especially in data analysis parts, needs careful thinking to check datasets and make sure the final results are correct. I had to pay even more attention during my internship because getting things right was essential. I am proud to be careful, but we must continually improve. Working on projects with complicated data sets and getting comments about my job will be part of how I improve more (Discover Data Science, 2023). As I move through my class, especially in parts that focus on hard data work, I expect to face problems. These will require more careful attention.

Independent Thinking

Thinking independently has been a vital skill learned from different schools and personal experiences. Working on research projects and doing my stuff has helped me learn more about myself. It also lets me question what people think is accurate or correct. We should always encourage thinking independently. I will look for chances to do my research and give extraordinary ideas in teamwork projects (Tableau, 2023). This is how I plan to keep growing. As I go deeper into more complex topics in my class, especially those that push creative ideas, I look forward to chances to improve and use my thinking-alone skills.

Section 4: Future Me Plan and Reflection

Long-term Aims Short-term Goals SMART Objectives
 1. Mastery in Advanced Data Science  a) Engage in advanced coursework and projects i. Sign up for two or more advanced data science classes beyond regular coursework. Concentrate on new technologies and techniques coming out soon.

ii. Finish every challenging class with a mark of B or better, showing that I fully understand the complex ideas taught.

iii. Check the requirements for a class and make sure I have or go beyond them. Set aside time every week to study and handle schoolwork and other school duties.

iv. Match the chosen classes with specific topics of interest in data science like machine learning, artificial intelligence or complex number analysis.

v. Sign up for my first big data science class before next semester ends and finish both classes in the coming school year.

 b) Attend relevant workshops and conferences i. Take part in at least three workshops and two big meetings about data science and technology changes within the next school year.

ii. Get papers showing I was there or participated in each meeting and event. These should prove that I played a significant role in those places.

iii. Find and learn about upcoming classes or meetings that match my studies and work goals. Plan my attendance early, thinking about time and money limits.

iv. Pick activities about new technologies, ways of doing things and what is popular in data science. Make sure they connect directly with my goals for my career.

v. Sign up for the first class in three months and attend at least one meeting this semester. Try to finish all activities before their deadline is reached.

 c) Build a solid professional network i. Get in touch with at least 15 professionals who work in the data science field at network gatherings, LinkedIn and question-and-answer interviews over two school semesters coming up.

ii. Make real friends with people who work by talking, getting advice and showing genuine interest in their stories. Try to get at least five experts on board for regular networking chats.

iii. Find possible connections using work groups, school networks and business happenings. Make a plan and set times for social events to ensure they go slowly and steadily.

iv. Focus on talking with people who know much about my dream job, like data scientists or researchers. These could be in companies that interest me, too.

v. Start talking to the first five experts in three months and keep growing my network. Talk with another ten people after that, within six more months.

 2. Leadership in Cross-functional Teams  a)Take on leadership roles in group projects i. Try to be a leader in at least two team projects next school year and how I can help the group work well together.

ii. Show I am in charge by setting up team jobs well, helping everyone talk, and ensuring projects end on time. I get input from my team members and project leaders to check how well I lead.

iii. At the start of group projects, show I want to be a leader. Work with my teammates to share tasks according to each person’s strengths and abilities.

iv. Experience leading a team makes it better at working together and talking with each other. This is very important for data science, where people must work as a group. It also shows how to handle projects well.

v. Get a leader job in the first group work task within four months and try to get another leader role in a future project during the school year.

 b) Join relevant clubs or societies  i. Join at least two data science or tech groups on my school campus within the next term.

ii. Go to at least two events or meetings held by each club or group every month, participate in talks and have chances to meet new people actively.

iii. Look for and learn about clubs or groups on campus that focus on data science or technology. Arrange when to participate in events, using my timetable and what I promised.

iv. Pick groups or teams that match the main topics of data science and technology. Join in activities that help improve skills and learn about different jobs.

v. Next month, Join a group or club and participate in their activities. Find and join a different group or club in the next two months.

 c) Seek mentorship from a professional leader  i. Find a professional leader in data science and ask them to be my mentor within two months.

ii. Set up monthly meetings or talks with the mentor, aiming for at least one monthly meeting. Make sure I have clear goals for my mentor talks, concentrate on improving skills and helping with work choices.

iii. Find possible teachers by looking at work friends, internet links or school alums. Write a nice message to show that I am interested in being mentored.

iv. Getting advice from a good boss in the data science world can help me learn, get guidance and make valuable connections. This helps my growth as both a person and a professional.

v. Start talking to a possible teacher this month, aiming for the first meeting with them in two months.

Reflection

I picked these long-term goals because I thought about my skills and how data science is changing. I want to be good at high-level data science because the field is changing quickly. By constantly working on difficult classes and projects and making connections, I want to stay ahead of what is new. Leading teams that work together aligns with my understanding of how vital teamwork skills are at a job. Getting involved in leading groups and finding someone to guide me will help me grow as a person. It also supports the work of any team I join.

References

Discover Data Science. (2023). Transferable Data Science Skills. DiscoverDataScience.org. https://www.discoverdatascience.org/articles/transferable-data-science-skills/

Patel, S. (2023). Why Communication Skills are Extremely Important for a data scientist? Www.linkedin.com. https://www.linkedin.com/pulse/why-communication-skills-extremely-important-data-scientist-patel

Tableau. (2023). 10 skill sets every data scientist should have. Tableau. https://www.tableau.com/learn/articles/data-science-skills

Employ A Systemic In A Non-Systemic Treatment Setting Seeking Case Supervision.

Jane Walker has been a drug and substance abuse user for quite a while, and she has presented herself to the therapy center for therapy sessions to help her cope with her addiction and an outstanding medical condition. She claims that she has been on cocaine for the better part of her life since her introduction to drugs when she was a teenager of 17 years. Her substance use traces back that she got introduced to the drug by her then friends, and with time, she found it difficult to stay without its use. Due to the prolonged drug use, she is exhibiting addiction side effects that make it difficult for her to survive without using the drug. This has made her seek medical attention and therapy to help her overcome this effect and help her gain her healthy track once again. Therefore, the medical attention and treatment sessions suit her since she is facing a serious problem that could lead to health challenges. Prolonged use of cocaine can cause cardiovascular diseases that could affect her later or even cause death if not managed well (Schwartz et al., 2022).

The systemic factors that have contributed to her addiction process are that during her introduction, she was still at a tender age, and her brain was not yet aware of the risk factors associated with the drug and substance abuse. Therefore, adolescents lack better decision-making judgments, and this lets them slip into drug addiction. Additionally, poverty contributes to addiction as some parents may find it difficult to let their children access therapy or seek medical advice shortly after realization of drug and substance abuse. Therefore, it becomes difficult for low-income families to provide basic needs as well as medical attention to support the individual’s recovery from addiction (Grinspoon, 2021). Jane found herself in this predicament, and hence, it was difficult for her to access medical attention and therapy sessions for her addiction until later, when the effects were felt. This led her to seek outside support to aid her in the recovery process; thus, she is a voluntary client. Therefore, she has been an intensive outpatient and is in her third session currently as one of the 30-90 days therapy models.

Looking at Jane’s current medical state of Jane, I would suggest using cognitive behavioral therapy (CBT). Thus, she needs to be monitored by the base nurse on the underlying condition, on how she is coping and recovering, and in addition, check for traces of cocaine in her blood system. Referring her to the nurse will help us know whether Jane is practicing self-control measures as a therapy coping mechanism. Moreover, it will provide us with a basis for how she is coping with the social skills. The CBT method will give us a clue on how she is monitoring her drug cravings and help us form a focus on how to help her better. With this method, we will also have a better understanding of her progress and will determine how we will improve the coping mechanism of drug abstinence (EMCDDA, 2014). Therefore, referring to her progress will determine whether to continue with the therapy medication or make any changes ethically without having to affect her mental state as per the code of ethics.

Research has shown that using systematic methods can be beneficial in ensuring that the children’s voices, views, and exercises are placed under supervision. The systemic approach borrows its roots in therapy as opposed to social work. This kind of literature review is complex and has a high level of focus on its ideas, qualifications, and practices. Most supervisors who are practicing are working in areas where there is a high-level practice framework based on the systemic approach. This means the approach pays more attention to interactions and relationships between people and those connected to the system. In this case, each individual is attached to another, and the main focus is how their interaction affects each other. This means that there should be a connection pattern in the entire world.

The only challenge I foresee working with the therapy center structure is that, with the 30-90 days structure, some patients may find it difficult to continue being motivated. Longer days moving from an outpatient to an inpatient may make them lose track of the self-control training measures, and they may relapse into drug and substance use. Additionally, finding a suitable sponsor may take time to provide support for their medical attention, and this may make the patient backslide to the drug. Therefore, providing a better strategic structure is essential so long as the patient is found fit for inpatient treatment to keep them motivated. Additionally, the state should provide more support to aid in the sponsorship of drug addict patients together with their families (Isobell et al., 2015) for a successful therapy session.

In conclusion, the systemic factors that have contributed to her addiction process are that during her introduction, she was still at a tender age, and her brain was not yet aware of the risk factors associated with drug and substance abuse. Therefore, adolescents lack better decision-making judgments, and this lets them slip into drug addiction.

References

European Monitoring Centre for Drugs and Drug Addiction (EMCDDA). (2014). Perspectives on drugs. Treatment for cocaine dependence: reviewing current evidence, pp. 1-4. https://doi.org/10.2165/00128415-201214190-00017

Grinspoon, P. (2021). Poverty, homelessness, and social stigma make addiction more deadly. Harvard Health Publishing. Retrieved from https://www.health.harvard.edu/blog/poverty-homelessness-and-social-stigma-make-addiction-more-deadly-202109282602

Isobell, D., Kamaloodien, K. & Savahl, S. (2015). A qualitative study of referring agents’ perceptions of access barriers to inpatient substance abuse treatment centers in the Western Cape. https://doi.org/10.1186/s12954-015-0064-z

Schwartz, E., Wolkowicz, N., Aquino, J. MacLean, R. & Sofuoglu, M. (2022). Cocaine Use Disorder (CUD): Current Clinical Perspectives. Substance Abuse and Rehabilitation, Vol. 13, pp. 25–46. https://doi.org/10.2147/sar.s337338