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2023 Week 1 Discussion Foundation of the Role of the Nurse Practitioner as Prescriber Legal and Professional
by adminNursing 2023 Discussion Pharmacology
Week 1 Discussion Foundation of the Role of the Nurse Practitioner as Prescriber Legal and Professional 2023 Assignment
Week 1: Discussion- Foundation of the Role of the Nurse Practitioner as Prescriber Legal and Professional Issues in Prescribi
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2023 Sallie Mae Fisher Health History Ms Fisher is an 82 year old female with a
by adminNursing 2023 Home Visit with Sallie Mae Fisher
Sallie Mae Fisher Health History Ms Fisher is an 82 year old female with a 2023 Assignment
Sallie Mae Fisher Health History Ms. Fisher is an 82-year-old female with a history of chronic congestive heart failure (CHF), atrial fibrillation, and hypertension. During the last 6 months, she has been hospitalized four times for exacerbation of her CHF. She was discharged home last Saturday from the hospital after a 3-day stay to treat increased dyspnea, an 8-pound weight gain, and chest pain. Ms. Fisher is recently widowed and lives alone. She has a daughter, Thelma Jean, who lives in town but works full time and has family issues of her own. Therefore, family support is limited. Hospital Discharge Instructions • Mountain Top Home Health to evaluate cardio-pulmonary status, medication management, and home safety. • Medical Equipment Company to deliver oxygen concentrator and instruct patient in use. O2 at 2 liters per nasal prongs PRN. • Prescriptions given at discharge: o Digoxin 0.25 mg once a day o Lasix 80 mg twice a day o Calan 240 mg once a day • Order written to continue other home meds. Sallie Mae’s Home Medication List • Zocar 50 mg once a day • Minipres 1 mg once a day • Vasotec 10 mg twice a day • Prilosec 20 mg once a day • Furosemide 40 mg once a day • Effexor 37.5 mg at bedtime • Lanoxin 0.125 mg every other day • Multivitamin once a day • Potassium 40 mEq once a day • Ibuprofen 400 mg q 4 hours as needed for pain • Darvocet N 100 mg q 4 hours as needed for pain • Nitroglycerin ointment, apply 1 inch every day
1. PROVIDE SUBSTANTIVE EVIDENCE (ASSESSMENT DATA) FOR EACH PROBLEM IDENTIFIED. (WRITE AN ESSAY 160 WORDS WITH)Prepare this step of the assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.
PLEASE REVIEW THE VIDEO
(http://lc.gcumedia.com/zwebassets/courseMaterialPages/nrs410v_vp01Alt.php).
PROBLEMS IDENTIFIED:
*POLYPHARMACY
*SAFETY
*NUTRITION
*MENTAL HEALTH
2. Utilizing the information write a scripted dialogue in which you provide Sallie Mae EDUCATION ABOUT THE SUBSTANTIVE EVIDENCE (ASSESSMENT DATA) FOR EACH PROBLEM IDENTIFIED.
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2023 Comment 1 Now that I am more familiar with the basic concepts of statistics I often see that
by adminNursing 2023 Please Do A Comment Base In This Answers. Write At Least 140 Words In Each Answer, Take Reference From (2013-2018) If Is Possible, Academic References Please Because The Teacher Check It Out One By One. SUSTANTIVE POST NEEDED. Thanks.
Comment 1 Now that I am more familiar with the basic concepts of statistics I often see that 2023 Assignment
Comment 1
Now that I am more familiar with the basic concepts of statistics, I often see that the statistical values are being used incorrectly. According to the authors of Abusing Statistics, some factors that may cause for a statistic to be used inappropriate include bad data collection, bad application of statistical functions, forming the wrong conclusion, and sneaky tricks to mislead the unwary. One example bad data collection is sampling the wrong population for a study. If you were trying to determine if teen pregnancies have increased from previous years, sampling women of a particular older age than teenage years would be inappropriate. An example of forming the wrong conclusion and utilizing sneaky tricks and misleading the unwary can be described similar to one of our previous discussion questions, statistics are used to the benefit of the researcher or the presenter. The values of the statistics can be manipulated into help accelerate a point of view of the presenter. With the researcher manipulating the data, it allows for applications for statistical functions. One example of this is when the researcher inputs their own assumptions to conclude the effects of a linear correlation.
Comment 2
On February 2007, CSPI (Center for Science in Public Interest) sued Coca-cola and Nestle for claiming their green tea caffeinated drink, Enviga, to be a “calorie burner”. The advertisement for Enviga said the drink burns more calories than actual intake which results “negative calories”. In other words, Coca Cola claimed Enviga will prevent extra calories from building up. This notion was based on a brief study on the drink funded by Coke and Nestle. The study consisted of 31 lean participants on calorie-restricted diet (participants were men and women) that have BMI of 22. CSPI included on the lawsuit that the study’s sample is biased and that it does not represent the American population where majority are either obese or overweight (BMI of 25 and above). We learned on the first week of class the importance of gathering data from a random sample that will most generalize the characteristics of the chosen population. Furthermore, the study claimed an individual to lose around 100 calories per day by drinking 3 cans of Enviga where mathematically, individual is expected to lose weight (with consistent consumption) after 35 days. The study’s length contradicts such claim because it was conducted no more than 72 hours (3 days). There is no research that will support long term effect of Enviga. In addition, the company claimed the green tea drink to have more epigallocatechin Gallate (EGCG) (which is a known cancer-fighting antioxidant) compared to other green tea beverages. Interestingly, Enviga contains aspartame as one of its ingredients which is known to cause blood-related cancers (American Cancer Society, 2018). Instead of falsely advertising the drink to have weight loss benefits, Coca Cola and Nestle should have (legally) labeled it a regular drink.
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2023 Comment 1 Linear correlation is a measure of dependence between two random
by adminNursing 2023 Please Do A Comment Base In This Answers. Write At Least 140 Words In Each Answer, Take Reference From (2013-2018) If Is Possible, Academic References Please Because The Teacher Check It Out One By One. SUSTANTIVE POST NEEDED. Thanks.
Comment 1 Linear correlation is a measure of dependence between two random 2023 Assignment
Comment 1
Linear correlation is a measure of dependence between two random variables. A linear correlation tells the strength of the relationship between the two variables. The results of the linear correlation analysis may be a positive correlation, a negative correlation, or no correlation. The conclusion of cigarettes cause the pulse rate to increase is not valid. The linear correlation does not give the results of the relationship between the two variables. Correlation does not imply causation in this sample. In a causal relationship the variables have an effect on each other and increases when one another do. In this study the only variables shown are smoking and heart rate. It shows no other variables to
consider such as gender, age, any health factors, or brands of cigarettes. Health factors along with other considerations may cause an increase in the pulse rate by itself. This sample does not give any indications of linear correlation between the two variables. There is no evidence in the statement “cigarettes cause the pulse rate to increase” to make it true without more evidence of both variables. Further research must be performed for the statement to valid. The research should include age, gender, health factors, and brands of cigarettes.
Comment 2
It seems like that the conclusion “Cigarettes cause the pulse rate to increase” is just not taking into consideration other variables and the heart rate can only increase so much. At that point, it would not matter how many more cigarettes a person smoked, the heart rate would not increase at the same rate. The linear correlation would change as the persons heart rate increases and the more cigarettes they smoked. The change in heart rate between the first and second cigarette will not be the same as the change in heart rate between the ninth and tenth cigarette. Identifying the associations between variables is the goal for correlational analyses. If the hypothesis is “Is there a relationship between the number of cigarettes smoked and pulse rate?”, then there is more room to talk about the relationship and arrive at a conclusion that yes, there is a relationship between the variables whether it is positive or negative. A better conclusion might be: There was a statistically significant correlation between the number of cigarettes smoked and pulse rate. This statement also needs to be backed up with results from a test like the Pearson product-moment correlation coefficient. Then the conclusion would be statistically relevant (Grove & Cipher, 2017).
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