Posts

Case Study: Mr. M. 

/0 Comments/in /by

Case Study: Mr. M.

 

It is necessary for an RN-BSN-prepared nurse to demonstrate an enhanced understanding of the pathophysiological processes of disease, the clinical manifestations and treatment protocols, and how they affect clients across the life span.

Evaluate the Health History and Medical Information for Mr. M., presented below.

Based on this information, formulate a conclusion based on your evaluation, and complete the Critical Thinking Essay assignment, as instructed below.

Health History and Medical Information

Health History

Mr. M., a 70-year-old male, has been living at the assisted living facility where you work. He has no know allergies. He is a nonsmoker and does not use alcohol. Limited physical activity related to difficulty ambulating and unsteady gait. Medical history includes hypertension controlled with ACE inhibitors, hypercholesterolemia, status post appendectomy, and tibial fracture status postsurgical repair with no obvious signs of complications. Current medications include Lisinopril 20mg daily, Lipitor 40mg daily, Ambien 10mg PRN, Xanax 0.5 mg PRN, and ibuprofen 400mg PRN.

Case Scenario

Over the past 2 months, Mr. M. seems to be deteriorating quickly. He is having trouble recalling the names of his family members, remembering his room number, and even repeating what he has just read. He is becoming agitated and aggressive quickly. He appears to be afraid and fearful when he gets aggressive. He has been found wandering at night and will frequently become lost, needing help to get back to his room. Mr. M has become dependent on many ADLs, whereas a few months ago he was fully able to dress, bathe, and feed himself. The assisted living facility is concerned with his rapid decline and has decided to order testing.

Objective Data

  1. Temperature: 37.1 degrees C
  2. BP 123/78 HR 93 RR 22 Pox 99%
  3. Denies pain
  4. Height: 69.5 inches; Weight 87 kg

Laboratory Results

  1. WBC: 19.2 (1,000/uL)
  2. Lymphocytes 6700 (cells/uL)
  3. CT Head shows no changes since the previous scan
  4. Urinalysis positive for a moderate amount of leukocytes and cloudy
  5. Protein: 7.1 g/dL; AST: 32 U/L; ALT 29 U/L

Critical Thinking Essay

In 750-1,000 words, critically evaluate Mr. M.’s situation. Include the following:

  1. Describe the clinical manifestations present in Mr. M.
  2. Based on the information presented in the case scenario, discuss what primary and secondary medical diagnoses should be considered for Mr. M. Explain why these should be considered and what data is provided for support.
  3. When performing your nursing assessment, discuss what abnormalities would you expect to find and why.
  4. Describe the physical, psychological, and emotional effects Mr. M.’s current health status may have on him. Discuss the impact it can have on his family.
  5. Discuss what interventions can be put into place to support Mr. M. and his family.
  6. Given Mr. M.’s current condition, discuss at least four actual or potential problems he faces. Provide a rationale for each

Political Analysis and Strategies

/0 Comments/in /by

Reaching out a Solution

This assignment is designed to assist you in developing a thoughtful process for advocating about an issue as a nurse, from identifying a problem that needs to be solved through articulating a process for doing so.

This assignment consists of answering each of the questions listed below from the “Political Analysis and Strategies” chapter of your course textbook. Write each question as a new topic area; then follow with a paragraph or two to answer the question. Be sure to use APA guidelines for writing style, spelling and grammar, and citation of sources, if any used. This project should be no longer than 4 pages.

Let us assume that you are a school nurse in a high school. At a recent school athletic event, a spectator suffered a cardiac arrest in the stands. A coach of the home team went into the high school to fetch the automatic emergency defibrillator (AED) only to find out that it was not readily available. In the meantime, an emergency squad arrived and resuscitated the spectator. On Monday morning, you learn of the absence of the AED only to find out that it had been locked in the custodian’s closet. Reflect on the following questions outlined in the “Political Analysis and Strategies” chapter:

  • What is the issue?
  • Is it my issue, and can I solve it?
  • Is this the real issue or merely a symptom of a larger one?
  • Does it need an immediate solution, or can it wait?
  • Is it likely to go away by itself?
  • Can I risk ignoring it?
  • What are the possible solutions? Are there risks to these solutions?
  • What steps would you need to take in order to solve the issue?
  • Does anyone else at the school need to be involved in the solution?
  • Where is the power leverage in the school to reach the preferred solution?

Reaching a solution requires the use of power vested in the nurse. Review Box 9-1 (Sources of Power) and determine which type(s) of power the school nurse has in this situation. State your reasons for your answer.

t competitor information categories

/0 Comments/in /by

Assignment:

Exercises:

  1. What competitor information categories are useful in competitor analysis? Are these categories appropriate for health care organizations? How can these information categories provide focus for information gathering and strategic decision making?
  2. Why is it important to clearly define the service area? How does managed care penetration affect service area definition?

Professional Development:

Conduct a service area competitor analysis within your community. Keep it fairly small in scope (i.e. laser hair removal business, extended care facility for ventilator-dependant patients, etc.) so that it is manageable. Begin by introducing the macro issues (general and health care) and then use this outline as an initial guide:

  1. Specify the Service Category
  2. Delineate the Service Area.
    1. General
    2. Economic
    3. Demographic
    4. Psychographic
    5. Health Status
  3. Perform a Service Area Structure Analysis
    1. Threat of New Entrants
    2. Intensity of Rivalry
    3. Threat of Substitutes
    4. Power of Customers
    5. Power of Suppliers
  4. Do a Competitor Analysis/Identify Service Category Critical Success Factors
    1. Competitor Strengths and Weaknesses
    2. Critical Success Factors
    3. Strategic Groups
    4. Map Competitors
    5. Likely Responses of Competitors
  5. Identify and Map Strategic Groups
  6. Provide a Synthesis.

Ginter, P.M., Duncan, W.J., & Swayne, L.E. (2013). Strategic management of health care organizations (7th ed.). San Francisco: Jossey-Bass.

electrical lines and internet

/0 Comments/in /by

Discussion: (Initial post of 400 words is due on Thursday

One thing in life we cannot control is nature….

  • What would your organization do if there was a natural disaster that destroyed electrical lines and internet servers?
  • How would you take care of your patients if you could not access the EMR for a week or more?
  • What recommendations can you make for improvement?

Part Two:

Assignment: This week is an APA essay of 1500 words with 500 words for each of the sections. Three scholarly sources are required. Be sure to write all APA papers/essays with correct title page, running head, page #s, introduction, level headings, a conclusion, APA citations, and a reference page. There are three different sections to the paper this week which should be separated using level headings to organize each section. The Rubric for grading this week is an Essay Rubric – please review.

  • Identify one aspect of big data and data mining that is interesting to you.
    • Explain the concept and how it might bring value to healthcare.
  • Describe the concept of continuity planning.
    • If you were the director or manager for your current workplace, describe the preparedness program you would recommend.
  • Locate an article discussing the use of informatics in healthcare education of the general public or of nursing students.
    • Discuss the benefits and drawbacks to using technology in this situation and recommendations from the author.
    • Do you feel this use of technology is a viable method of educating (the public or nursing students)? Why or why not?

Calculating Simple Linear Regression

/0 Comments/in /by
Exercise 29

Calculating Simple Linear Regression

Simple linear regression is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (Cohen & Cohen, 1983). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in Exercise 14, Figure 14-1 illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.

A regression equation can be generated with a data set containing subjects’ x and y values. Once this equation is generated, it can be used to predict future subjects’ y values, given only their x values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable y (dependent variable, or outcome) is predicted from the same subject’s known score on variable x (independent variable, or predictor).

Research Designs Appropriate for Simple Linear Regression

Research designs that may utilize simple linear regression include any associational design (Gliner et al., 2009). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.

Statistical Formula and Assumptions

Use of simple linear regression involves the following assumptions (Zar, 2010):

1. Normal distribution of the dependent (y) variable

2. Linear relationship between x and y

3. Independent observations

4. No (or little) multicollinearity

5. Homoscedasticity

320

Data that are homoscedastic are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of x, the distribution of y values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (Cohen & Cohen, 1983; Zar, 2010). Visual examples of homoscedasticity and heteroscedasticity are presented in Exercise 30.

In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters a and b are computed to obtain a regression equation. To understand the mathematical process, recall the algebraic equation for a straight line:

y=bx+a

image

where

y=the dependent variable(outcome)

image

x=the independent variable(predictor)

image

b=the slope of the line

image

a=y-intercept(the point where the regression line intersects the y-axis)

image

No single regression line can be used to predict with complete accuracy every y value from every x value. In fact, you could draw an infinite number of lines through the scattered paired values (Zar, 2010). However, the purpose of the regression equa­tion is to develop the line to allow the highest degree of prediction possible—the line of best fit. The procedure for developing the line of best fit is the method of least squares. The formulas for the beta (β) and slope (α) of the regression equation are computed as follows. Note that once the β is calculated, that value is inserted into the formula for α.

β=n∑xy−∑x∑yn∑x 2 −(∑x) 2

image

α=∑y−b∑xn

image

Hand Calculations

This example uses data collected from a study of students enrolled in a registered nurse to bachelor of science in nursing (RN to BSN) program (Mancini, Ashwill, & Cipher, 2014). The predictor in this example is number of academic degrees obtained by the student prior to enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is “Number of degrees does not predict the number of months until completion of an RN to BSN program.”

The data are presented in Table 29-1. A simulated subset of 20 students was selected for this example so that the computations would be small and manageable. In actuality, studies involving linear regression need to be adequately powered (Aberson, 2010; Cohen, 1988). Observe that the data in Table 29-1 are arranged in columns that correspond to 321the elements of the formula. The summed values in the last row of Table 29-1 are inserted into the appropriate place in the formula for b.

TABLE 29-1

ENROLLMENT GPA AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAM

Student ID x y x2 xy
(Number of Degrees) (Months to Completion)
1 1 17 1 17
2 2 9 4 18
3 0 17 0 0
4 1 9 1 9
5 0 16 0 0
6 1 11 1 11
7 0 15 0 0
8 0 12 0 0
9 1 15 1 15
10 1 12 1 12
11 1 14 1 14
12 1 10 1 10
13 1 17 1 17
14 0 20 0 0
15 2 9 4 18
16 2 12 4 24
17 1 14 1 14
18 2 10 4 20
19 1 17 1 17
20 2 11 4 22
sum Σ 20 267 30 238

image

The computations for the b and α are as follows:

Step 1: Calculate b.
From the values in Table 29-1, we know that n = 20, Σx = 20, Σy = 267, Σx2 = 30, and Σxy = 238. These values are inserted into the formula for b, as follows:

b=20(238)−(20)(267)20(30)−20 2

image

b=−2.9

image

Step 2: Calculate α.
From Step 1, we now know that b = −2.9, and we plug this value into the formula for α.

α=267−(−2.9)(20)20

image

α=16.25

image

Step 3: Write the new regression equation:

y=−2.9x+16.25

image

322

Step 4: Calculate R.
The multiple R is defined as the correlation between the actual y values and the predicted y values using the new regression equation. The predicted y value using the new equation is represented by the symbol ŷ to differentiate from y, which represents the actual y values in the data set. We can use our new regression equation from Step 3 to compute predicted program completion time in months for each student, using their number of academic degrees prior to enrollment in the RN to BSN Program. For example, Student #1 had earned 1 academic degree prior to enrollment, and the predicted months to completion for Student 1 is calculated as:

y ̂ =−2.9(1)+16.25

image

y ̂ =13.35

image

Thus, the predicted ŷ is 13.35 months. This procedure would be continued for the rest of the students, and the Pearson correlation between the actual months to completion (y) and the predicted months to completion (ŷ) would yield the multiple R value. In this example, the R = 0.638. The higher the R, the more likely that the new regression equation accurately predicts y, because the higher the correlation, the closer the actual y values are to the predicted ŷ values. Figure 29-1 displays the regression line where the x axis represents possible numbers of degrees, and the y axis represents the predicted months to program completion (ŷ values).

image

FIGURE 29-1  REGRESSION LINE REPRESENTED BY NEW REGRESSION EQUATION.

Step 5: Determine whether the predictor significantly predicts y.

t=Rn−21−R 2   ‾ ‾ ‾ ‾  √

image

To know whether the predictor significantly predicts y, the beta must be tested against zero. In simple regression, this is most easily accomplished by using the R value from Step 4:

t=.638200−21−.407  ‾ ‾ ‾ ‾ ‾  √

image

t=3.52

image

323

The t value is then compared to the t probability distribution table (see Appendix A). The df for this t statistic is n − 2. The critical t value at alpha (α) = 0.05, df = 18 is 2.10 for a two-tailed test. Our obtained t was 3.52, which exceeds the critical value in the table, thereby indicating a significant association between the predictor (x) and outcome (y).

Step 6: Calculate R2.
After establishing the statistical significance of the R value, it must subsequently be examined for clinical importance. This is accomplished by obtaining the coefficient of determination for regression—which simply involves squaring the R value. The R2 represents the percentage of variance explained in y by the predictor. Cohen describes R2 values of 0.02 as small, 0.15 as moderate, and 0.26 or higher as large effect sizes (Cohen, 1988). In our example, the R was 0.638, and, therefore, the R2 was 0.407. Multiplying 0.407 × 100% indicates that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at admission (Cohen & Cohen, 1983).
The R2 can be very helpful in testing more than one predictor in a regression model. Unlike R, the R2 for one regression model can be compared with another regression model that contains additional predictors (Cohen & Cohen, 1983). The R2 is discussed further in Exercise 30.
The standardized beta (β) is another statistic that represents the magnitude of the association between x and y. β has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. This value can be calculated by hand but is best computed with statistical software. The standardized beta (β) is calculated by converting the x and y values to z scores and then correlating the x and y value using the Pearson r formula. The standardized beta (β) is often reported in literature instead of the unstandardized b, because b does not have lower or upper limits and therefore the magnitude of b cannot be judged. β, on the other hand, is interpreted as a Pearson r and the descriptions of the magnitude of β can be applied, as recommended by Cohen (1988). In this example, the standardized beta (β) is −0.638. Thus, the magnitude of the association between x and y in this example is considered a large predictive association (Cohen, 1988).

324

SPSS Computations

This is how our data set looks in SPSS.

image

Step 1: From the “Analyze” menu, choose “Regression” and “Linear.”

Step 2: Move the predictor, Number of Degrees, to the space labeled “Independent(s).” Move the dependent variable, Number of Months to Completion, to the space labeled “Dependent.” Click “OK.”

image

325

Interpretation of SPSS Output

The following tables are generated from SPSS. The first table contains the multiple R and the R2 values. The multiple R is 0.638, indicating that the correlation between the actual y values and the predicted y values using the new regression equation is 0.638. The R2 is 0.407, indicating that 40.7% of the variance in months to program completion can be explained by knowing the student’s number of earned academic degrees at enrollment.

Regression

image

The second table contains the ANOVA table. As presented in Exercises 18 and 33, the ANOVA is usually performed to test for differences between group means. However, ANOVA can also be performed for regression, where the null hypothesis is that “knowing the value of x explains no information about y”. This table indicates that knowing the value of x explains a significant amount of variance in y. The contents of the ANOVA table are rarely reported in published manuscripts, because the significance of each predictor is presented in the last SPSS table titled “Coefficients” (see below).

image

The third table contains the b and a values, standardized beta (β), t, and exact p value. The a is listed in the first row, next to the label “Constant.” The β is listed in the second row, next to the name of the predictor. The remaining information that is important to extract when interpreting regression results can be found in the second row. The standardized beta (β) is −0.638. This value has limits just like a Pearson r, meaning that the standardized β cannot be lower than −1.00 or higher than 1.00. The t value is −3.516, and the exact p value is 0.002.

image
326

Final Interpretation in American Psychological Association (APA) Format

The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). Simple linear regression was performed with number of earned academic degrees as the predictor and months to program completion as the dependent variable. The student’s number of degrees significantly predicted months to completion among students in an RN to BSN program, β = −0.638, p = 0.002, and R2 = 40.7%. Higher numbers of earned academic degrees significantly predicted shorter program completion time.

327

Study Questions

1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for months to completion (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

2. State the null hypothesis for the example where number of degrees was used to predict time to BSN program completion.

3. In the formula y = bx + a, what does “b” represent?

4. In the formula y = bx + a, what does “a” represent?

5. Using the new regression equation, ŷ = −2.9x + 16.25, compute the predicted months to program completion if a student’s number of earned degrees is 0. Show your calculations.

6. Using the new regression equation, ŷ = −2.9x + 16.25, compute the predicted months to program completion if a student’s number of earned degrees is 2. Show your calculations.

328

7. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

8. What was the exact likelihood of obtaining a t value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

9. How much variance in months to completion is explained by knowing the student’s number of earned degrees?

10. How would you characterize the magnitude of the R2 in the example? Provide a rationale for your answer.

329

Answers to Study Questions

1. The Shapiro-Wilk p value for months to RN to BSN program completion was 0.16, indicating that the frequency distribution did not significantly deviate from normality. Moreover, visual inspection of the frequency distribution indicates that months to completion is approximately normally distributed. See SPSS output below for the histograms of the distribution:

image

2. The null hypothesis is: “The number of earned academic degrees does not predict the number of months until completion of an RN to BSN program.”

3. In the formula y = bx + a, “b” represents the slope of the regression line.

4. In the formula y = bx + a, “a” represents the y-intercept, or the point at which the regression line intersects the y-axis.

5. The predicted months to program completion if a student’s number of academic degrees is 0 is calculated as: ŷ = −2.9(0) + 16.25 = 16.25 months.

6. The predicted months to program completion if a student’s number of academic degrees is 2 is calculated as: ŷ = −2.9(2) + 16.25 = 10.45 months.

7. The correlation between the actual y values and the predicted y values using the new regression equation in the example, also known as the multiple R, is 0.638.

8. The exact likelihood of obtaining a t value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true, was 0.2%. This value was obtained by looking at the SPSS output table titled “Coefficients” in the last value of the column labeled “Sig.”

9. 40.7% of the variance in months to completion is explained by knowing the student’s number of earned academic degrees at enrollment.

10. The magnitude of the R2 in this example, 0.407, would be considered a large effect according to the effect size tables in Exercises 24 and 25.

330

Data for Additional Computational Practice for the Questions to be Graded

Using the example from Mancini and colleagues (2014), students enrolled in an RN to BSN program were assessed for demographics at enrollment. The predictor in this example is age at program enrollment, and the dependent variable was number of months it took for the student to complete the RN to BSN program. The null hypothesis is: “Student age at enrollment does not predict the number of months until completion of an RN to BSN program.” The data are presented in Table 29-2. A simulated subset of 20 students was randomly selected for this example so that the computations would be small and manageable.

TABLE 29-2

AGE AT ENROLLMENT AND MONTHS TO COMPLETION IN AN RN TO BSN PROGRAM

Student ID x y x2 xy
(Student Age) (Months to Completion)
1 23 17 529 391
2 24 9 576 216
3 24 17 576 408
4 26 9 676 234
5 31 16 961 496
6 31 11 961 341
7 32 15 1,024 480
8 33 12 1,089 396
9 33 15 1,089 495
10 34 12 1,156 408
11 34 14 1,156 476
12 35 10 1,225 350
13 35 17 1,225 595
14 39 20 1,521 780
15 40 9 1,600 360
16 42 12 1,764 504
17 42 14 1,764 588
18 44 10 1,936 440
19 51 17 2,601 867
20 24 11 576 264
sum Σ 677 267 24,005 9,089

image

331

EXERCISE 29 Questions to Be Graded

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/Statistics/ under “Questions to Be Graded.”

1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?

2. State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.

3. What is b as computed by hand (or using SPSS)?

4. What is a as computed by hand (or using SPSS)?

332

5. Write the new regression equation.

6. How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.

7. How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age?

8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Given the results of your analyses, would you use the calculated regression equation to predict future students’ program completion time by using enrollment age as x? Provide a rationale for your answer

(Grove 319-332)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

 

Exercise 35

Calculating Pearson Chi-Square

The Pearson chi-square test (χ2) compares differences between groups on variables measured at the nominal level. The χ2 compares the frequencies that are observed with the frequencies that are expected. When a study requires that researchers compare proportions (percentages) in one category versus another category, the χ2 is a statistic that will reveal if the difference in proportion is statistically improbable.

A one-way χ2 is a statistic that compares different levels of one variable only. For example, a researcher may collect information on gender and compare the proportions of males to females. If the one-way χ2 is statistically significant, it would indicate that proportions of one gender are significantly higher than proportions of the other gender than what would be expected by chance (Daniel, 2000). If more than two groups are being examined, the χ2 does not determine where the differences lie; it only determines that a significant difference exists. Further testing on pairs of groups with the χ2 would then be warranted to identify the significant differences.

A two-way χ2 is a statistic that tests whether proportions in levels of one nominal variable are significantly different from proportions of the second nominal variable. For example, the presence of advanced colon polyps was studied in three groups of patients: those having a normal body mass index (BMI), those who were overweight, and those who were obese (Siddiqui, Mahgoub, Pandove, Cipher, & Spechler, 2009). The research question tested was: “Is there a difference between the three groups (normal weight, overweight, and obese) on the presence of advanced colon polyps?” The results of the χ2 test indicated that a larger proportion of obese patients fell into the category of having advanced colon polyps compared to normal weight and overweight patients, suggesting that obesity may be a risk factor for developing advanced colon polyps. Further examples of two-way χ2 tests are reviewed in Exercise 19.

Research Designs Appropriate for the Pearson χ2

Research designs that may utilize the Pearson χ2 include the randomized experimental, quasi-experimental, and comparative designs (Gliner, Morgan, & Leech, 2009). The variables may be active, attributional, or a combination of both. An active variable refers to an intervention, treatment, or program. An attributional variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. Regardless of the whether the variables are active or attributional, all variables submitted to χ2 calculations must be measured at the nominal level.

410

Statistical Formula and Assumptions

Use of the Pearson χ2 involves the following assumptions (Daniel, 2000):

1. Only one datum entry is made for each subject in the sample. Therefore, if repeated measures from the same subject are being used for analysis, such as pretests and posttests, χ2 is not an appropriate test.

2. The variables must be categorical (nominal), either inherently or transformed to categorical from quantitative values.

3. For each variable, the categories are mutually exclusive and exhaustive. No cells may have an expected frequency of zero. In the actual data, the observed cell frequency may be zero. However, the Pearson χ2 test is sensitive to small sample sizes, and other tests, such as the Fisher’s exact test, are more appropriate when testing very small samples (Daniel, 2000; Yates, 1934).

The test is distribution-free, or nonparametric, which means that no assumption has been made for a normal distribution of values in the population from which the sample was taken (Daniel, 2000).

The formula for a two-way χ2 is:

χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)

image

The contingency table is labeled as follows. A contingency table is a table that displays the relationship between two or more categorical variables (Daniel, 2000):

A B
C D

With any χ2 analysis, the degrees of freedom (df) must be calculated to determine the significance of the value of the statistic. The following formula is used for this calculation:

df=(R−1)(C−1)

image

where

R=Number of rows

image

C=Number of columns

image

Hand Calculations

A retrospective comparative study examined whether longer antibiotic treatment courses were associated with increased antimicrobial resistance in patients with spinal cord injury (Lee et al., 2014). Using urine cultures from a sample of spinal cord–injured veterans, two groups were created: those with evidence of antibiotic resistance and those with no evidence of antibiotic resistance. Each veteran was also divided into two groups based on having had a history of recent (in the past 6 months) antibiotic use for more than 2 weeks or no history of recent antibiotic use.

411

The data are presented in Table 35-1. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”

TABLE 35-1

ANTIBIOTIC RESISTANCE BY ANTIBIOTIC USE

Antibiotic Use No Recent Use
Resistant 8 7
Not resistant 6 21

The computations for the Pearson χ2 test are as follows:

Step 1: Create a contingency table of the two nominal variables:

Used Antibiotics No Recent Use Totals
Resistant 8 7 15
Not resistant 6 21 27
Totals 14 28 42 ←Total n

image

Step 2: Fit the cells into the formula:

χ 2 =n[(A)(D)−(B)(C)] 2 (A+B)(C+D)(A+C)(B+D)

image

χ 2 =42[(8)(21)−(7)(6)] 2 (8+7)(6+21)(8+6)(7+21)

image

χ 2 =666,792158,760

image

χ 2 =4.20

image

Step 3: Compute the degrees of freedom:

df=(2−1)(2−1)=1

image

Step 4: Locate the critical χ2 value in the χ2 distribution table (Appendix D) and compare it to the obtained χ2 value.

The obtained χ2 value is compared with the tabled χ2 values in Appendix D. The table includes the critical values of χ2 for specific degrees of freedom at selected levels of significance. If the value of the statistic is equal to or greater than the value identified in the χ2 table, the difference between the two variables is statistically significant. The critical χ2 for df = 1 is 3.84, and our obtained χ2 is 4.20, thereby exceeding the critical value and indicating a significant difference between antibiotic users and non-users on the presence of antibiotic resistance.

Furthermore, we can compute the rates of antibiotic resistance among antibiotic users and non-users by using the numbers in the contingency table from Step 1. The antibiotic resistance rate among the antibiotic users can be calculated as 8 ÷ 14 = 0.571 × 100% = 57.1%. The antibiotic resistance rate among the non-antibiotic users can be calculated as 7 ÷ 28 = 0.25 × 100% = 25%.

412

SPSS Computations

The following screenshot is a replica of what your SPSS window will look like. The data for subjects 24 through 42 are viewable by scrolling down in the SPSS screen.

image

413

Step 1: From the “Analyze” menu, choose “Descriptive Statistics” and “Crosstabs.” Move the two variables to the right, where either variable can be in the “Row” or “Column” space.

image

Step 2: Click “Statistics” and check the box next to “Chi-square.” Click “Continue” and “OK.”

image

414

Interpretation of SPSS Output

The following tables are generated from SPSS. The first table contains the contingency table, similar to Table 35-1 above. The second table contains the χ2 results.

Crosstabs

image

image

The last table contains the χ2 value in addition to other statistics that test associations between nominal variables. The Pearson χ2 test is located in the first row of the table, which contains the χ2 value, df, and p value.

Final Interpretation in American Psychological Association (APA) Format

The following interpretation is written as it might appear in a research article, formatted according to APA guidelines (APA, 2010). A Pearson χ2 analysis indicated that antibiotic users had significantly higher rates of antibiotic resistance than those who did not use antibiotics, χ2(1) = 4.20, p = 0.04 (57.1% versus 25%, respectively). This finding suggests that extended antibiotic use may be a risk factor for developing resistance, and further research is needed to investigate resistance as a direct effect of antibiotics.

415

Study Questions

1. Do the example data meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

2. What is the null hypothesis in the example?

3. What was the exact likelihood of obtaining a χ2 value at least as extreme or as close to the one that was actually observed, assuming that the null hypothesis is true?

4. Using the numbers in the contingency table, calculate the percentage of antibiotic users who were resistant.

5. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who were resistant.

6. Using the numbers in the contingency table, calculate the percentage of resistant veterans who used antibiotics for more than 2 weeks.

416

7. Using the numbers in the contingency table, calculate the percentage of resistant veterans who had no history of antibiotic use.

8. What kind of design was used in the example?

9. What result would have been obtained if the variables in the SPSS Crosstabs window had been switched, with Antibiotic Use being placed in the “Row” and Resistance being placed in the “Column”?

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

417

Answers to Study Questions

1. Yes, the data meet the assumptions of the Pearson χ2:

a. Only one datum per participant was entered into the contingency table, and no participant was counted twice.

b. Both antibiotic use and resistance are categorical (nominal-level data).

c. For each variable, the categories are mutually exclusive and exhaustive. It was not possible for a participant to belong to both groups, and the two categories (recent antibiotic user and non-user) included all study participants.

2. The null hypothesis is: “There is no difference between antibiotic users and non-users on the presence of antibiotic resistance.”

3. The exact likelihood of obtaining a χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true, was 4.0%.

4. The percentage of antibiotic users who were resistant is calculated as 8 ÷ 14 = 0.5714 × 100% = 57.14% = 57.1%.

5. The percentage of non-antibiotic users who were resistant is calculated as 7 ÷ 28 = 0.25 × 100% = 25%.

6. The percentage of antibiotic-resistant veterans who used antibiotics for more than 2 weeks is calculated as 8 ÷ 15 = 0.533 × 100% = 53.3%.

7. The percentage of resistant veterans who had no history of antibiotic use is calculated as 6 ÷ 27 = 0.222 × 100% = 22.2%.

8. The study design in the example was a retrospective comparative design (Gliner et al., 2009).

9. Switching the variables in the SPSS Crosstabs window would have resulted in the exact same χ2 result.

10. The sample size was adequate to detect differences between the two groups, because a significant difference was found, p = 0.04, which is smaller than alpha = 0.05.

418

Data for Additional Computational Practice for Questions to be Graded

A retrospective comparative study examining the presence of candiduria (presence of Candida species in the urine) among 97 adults with a spinal cord injury is presented as an additional example. The differences in the use of antibiotics were investigated with the Pearson χ2 test (Goetz, Howard, Cipher, & Revankar, 2010). These data are presented in Table 35-2 as a contingency table.

TABLE 35-2

CANDIDURIA AND ANTIBIOTIC USE IN ADULTS WITH SPINAL CORD INJURIES

Candiduria No Candiduria Totals
Antibiotic use 15 43 58
No antibiotic use 0 39 39
Totals 15 82 97

image

419

EXERCISE 35 Questions to Be Graded

Name: _______________________________________________________ Class: _____________________

Date: ___________________________________________________________________________________

Follow your instructor’s directions to submit your answers to the following questions for grading. Your instructor may ask you to write your answers below and submit them as a hard copy for grading. Alternatively, your instructor may ask you to use the space below for notes and submit your answers online at http://evolve.elsevier.com/Grove/statistics/ under “Questions to Be Graded.”

1. Do the example data in Table 35-2 meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.

2. Compute the χ2 test. What is the χ2 value?

3. Is the χ2 significant at α = 0.05? Specify how you arrived at your answer.

4. If using SPSS, what is the exact likelihood of obtaining the χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

420

5. Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.

6. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria.

7. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.

8. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use.

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

(Grove 409-420)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Each exercise has 10 questions at the end which says Questions to be graded.I need those questions to be answer.

hypersensitivity reactions

/0 Comments/in /by

Discussion Prompt 1COLLAPSE

Review the following case study and discuss the questions that follow.

EO is an 8-year-old girl with a history of asthma and allergy to bee stings. She has been brought to the clinic complaining of a throat infection. Her health care provider prescribes a course of penicillin to manage her current infection and cautions her parents to watch her closely for a reaction.

  1. What type of reaction is the health care provider concerned about, and why?
  2. Explain the role of IgE and mast cells in type I hypersensitivity reactions. Why might EO react adversely to the antibiotic with the first use?
  3. What would you tell EO’s parents to look for when they are assessing for a reaction?

health screening and history.

/0 Comments/in /by

In this assignment, you will be completing a comprehensive health screening and history on a young adult. To complete this assignment, do the following:

Select an adolescent or young adult client on whom to perform a health screening and history. Students who do not work in an acute setting may “practice” these skills with a patient, community member, neighbor, friend, colleague, or loved one.

Complete the “Health History and Screening of an Adolescent or Young Adult Client” worksheet.

Complete the assignment as outlined on the worksheet, including:

  1. Biographical data
  2. Past health history
  3. Family history: Obstetrics history (if applicable) and well young adult behavioral health history screening
  4. Review of systems
  5. All components of the health history
  6. Three nursing diagnoses for this client based on the health history and screening (one actual nursing diagnosis, one wellness nursing diagnosis, and one “risk for” nursing diagnosis)
  7. Rationale for the choice of each nursing diagnosis.
  8. A wellness plan for the adolescent/young adult client, using the three nursing diagnoses you have identified.

Format the write-up in a manner that is easily read, computer-generated, neat, and without spelling errors. Use correct acronyms or abbreviations when indicated.

While APA format is not required for the body of this assignment, solid academic writing is expected and in-text citations and references should be presented using APA documentation guidelines, which can be found in the APA Style Guide, located in the Student Success Center.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are not required to submit this assignment to Turnitin.

medical term

/0 Comments/in /by

Multiple question and matching questions need done for Tuesday 12/12/2017 by 2:00pm

Question 1.The suffix of a medical term is found:

after the combining vowel.

at the beginning of the term.

at the end of the term.

at the middle of the term.

Question 2. Which of the following is a use for a combining vowel?

To ensure that non-medical personnel are confused

Splitting up word parts

Making words trickier to spell

Making words easier to pronounce

Question 3. The prefix in pansinusitis means:

none.

inflammation.

sinuses.

all.

Question 4. Which of the following is a combining form?

Posthepatic

Hepatitis

Hepat/o

Prehepatic

Question 5. The meaning of condition of the blood is translated into a medical term by the suffix:

–algia.

–cele.

–emesis.

–emia.

Question 6.Match the term component on the left with the description of what it represents on the right.

–itis.                         word root

prefix

 

suffix

bi-

–plasty

cardi

Question 7. Match the term component on the left with the description of what it represents on the right.

A.

first

B.

view of

C.

one

D.

excess

E.

record

F.

thirst

G.

process of visually examining

–opsy

–dipsia

– gram

uni

hyper-

primi-

Question 8. Describe strategies that healthcare personnel can use to define and build medical terms.

Your response must be at least 200 words in length.

Drug Enforcement Administration

/0 Comments/in /by
  •  In 2-3 pages:
  • Describe the role of the Drug Enforcement Administration (DEA) as it pertains to the PMHNP.
  • Explain your responsibilities when having a DEA number.
  • Explain how you apply for a DEA number.
  • Explain your state’s requirements for a safe prescribing and prescription monitoring program. Explain your responsibility as a PMHNP to follow these requirements.
  • Provide an example of a drug you may prescribe from each of the Schedule II-V drug levels.

Assessing Client Families

/0 Comments/in /by

  Practicum – Assessing Client Families

To prepare:

· Select a client family that you have observed or counseled at your practicum site.

· Review pages 137–142 of Wheeler (2014) and the Hernandez Family Genogram

video in this week’s Learning Resources. (SEE ATTACHED VIDEO TRANSCRIPT)

· Reflect on elements of writing a comprehensive client assessment and creating a

genogram for the client you selected.

                                                                         The Assignment

                                          Part 1: Comprehensive Client Family Assessment

Create a comprehensive client assessment for your selected client family that addresses (without violating HIPAA regulations) the following:

· Demographic information

· Presenting problem

· History or present illness

· Past psychiatric history

· Medical history

· Substance use history

· Developmental history

· Family psychiatric history

· Psychosocial history

· History of abuse and/or trauma

· Review of systems

· Physical assessment

· Mental status exam

· Differential diagnosis

· Case formulation

· Treatment plan

                                                Part 2: Family Genogram

Develop a genogram for the client family you selected. The genogram should extend back at least three generations (parents, grandparents, and great grandparents).

N:B. (1)PLEASE THIS ASSIGNMENT HAS 2 PARTS, AND I HAVE ATTACHED A SAMPLE OF THE ASSIGNMENT, BUT THE SAMPLE TALKS ONLY ABOUT HERNANDEZ, BUT THIS ASSIGNMENT IS FOCUS ON HERNANDEZ FAMILY.

(2). HERNANDEZ FAMILY GENOGRAM VIDEO TRANSCRIPT IS ATTACHED INCASE YOU CAN NOT VIEW THE VIDEO

                                                      Learning Resources

Required Readings

Nichols, M. (2014). The essentials of family therapy (6th ed.). Boston, MA: Pearson.

  • Chapter 8, “Experiential      Family Therapy” (pp. 129–147)
  • Chapter 13, “Narrative Therapy” (pp. 243–258)

Wheeler, K. (Ed.). (2014). Psychotherapy for the advanced practice psychiatric nurse: A how-to guide for evidence-based practice. New York, NY: Springer.

  • “Genograms” pp. 137-142

Cohn, A. S. (2014). Romeo and Julius: A narrative therapy intervention for sexual-minority couples. Journal of Family Psychotherapy, 25(1), 73–77. doi:10.1080/08975353.2014.881696

Escudero, V., Boogmans, E., Loots, G., & Friedlander, M. L. (2012). Alliance rupture and repair in conjoint family therapy: An exploratory study. Psychotherapy, 49(1), 26–37. doi:10.1037/a0026747

Freedman, J. (2014). Witnessing and positioning: Structuring narrative therapy with families and couples. Australian & New Zealand Journal of Family Therapy, 35(1), 20–30. doi:10.1002/anzf.1043

Phipps, W. D., & Vorster, C. (2011). Narrative therapy: A return to the intrapsychic perspective. Journal of Family Psychotherapy, 22(2), 128–147. doi:10.1080/08975353.2011.578036

Saltzman, W. R., Pynoos, R. S., Lester, P., Layne, C. M., & Beardslee, W. R. (2013). Enhancing family resilience through family narrative co-construction. Clinical Child and Family Psychology Review, 16(3), 294–310. doi:10.1007/s10567-013-0142-2

                                                    Required Media

Governors State University (Producer). (2009). Emotionally focused couples therapy [Video file]. Chicago, IL: Author.

 

Laureate Education (Producer). (2013b). Hernandez family genogram [Video file]. Baltimore, MD: Author. (SEE ATTACHED VIDEO TRANSCRIPT)

Psychotherapy.net (Producer). (1998). Narrative family therapy [Video file]. San Francisco, CA: Author.