The nurse would give immediate attention to the patient who presents with which complaint?

Following my regression model that measures the effect of gender, marital status, residence, and education on wages, I use the following command in stata: margins urban  What does my output represent?  ——————————————————————————             |            Delta-method             |     Margin   std. err.      t    P>|t|     ————-+—————————————————————-       urban |          0  |   5.168331    .263554    19.61   0.000     4.650572     5.68609          1  |    6.17572   .1610015    38.36   0.000     5.859428    6.492012——————————————————————————      

Based on this margins output, which category has the highest predicted wages?    . margins urban#femalePredictive margins                                         Number of obs = 526Model VCE: OLSExpression: Linear prediction, predict()——————————————————————————             |            Delta-method             |     Margin   std. err.      t    P>|t|     ————-+—————————————————————-urban#female |        0 0  |   6.175998   .2930057    21.08   0.000      5.60038    6.751615        0 1  |   4.072693   .3022079    13.48   0.000     3.478997    4.666389        1 0  |   7.183387   .2093609    34.31   0.000     6.772092    7.594683        1 1  |   5.080082   .2147998    23.65   0.000     4.658102    5.502063——————————————————————————

I run a regression in stata with the following variables: wage = hourly wage married = dummy variable =1 for married, =0 for nonmarried  female = dummy variable =1 for female, =0 for male  tenure = number of years on the job  tenursq = tenure squared  My results are shown below:  Table 1. OLS Regression of Wage on Marital Status, Sex, and Tenure Variable Coefficient Std. Error t-value p-value Married (1 = married) 0.784 0.306 2.56 0.011 Female (1 = female) –1.976 0.293 –6.74 0.000 Tenure 0.278 0.052 5.31 0.000 Tenure² –0.0055 0.0019 –2.96 0.003 Constant 5.379 0.306 17.60 0.000 Model Statistics Number of observations: 526 Adjusted R-squared: 0.218 F-statistic: 37.51 (df = 4, 521)   Question: Based on the coefficient for married, what is the substantive interpretation? 

A patient contacts the office and tells the nurse that she is worried about her 10-year-old daughter’s breast development. She describes the right breast as being larger than the left breast. What would be the nurse’s best response?

From the prior regression, I decide to test whether there is an interaction between marital status and sex in my wage model.  Based on the results below, what is the reference category for the interaction?  Table 2. OLS Regression of Wage with Marriage × Female Interaction Variable Coefficient Std. Error t-value p-value Married#Female 0    1 –0.535 0.455 –1.18 0.240 1    0 2.061 0.434 4.75 0.000 1    1 –0.881 0.449 –1.96 0.050 Tenure 0.260 0.052 5.03 0.000 Tenure² –0.00516 0.00184 –2.81 0.005 Constant 4.579 0.359 12.76 0.000  

The nurse is assisting with a breast self exam (BSE) clinic. Which of these women reflect abnormal findings during the inspection phase of breast examination?

An elderly female patient remarks that she just cannot believe that her breasts “sag so much.” She states it must be from a lack of exercise. What explanation should the nurse offer her?

During an annual physical examination, a 43-year-old patient states that she does not perform monthly breast self-examinations (BSEs). She tells the nurse that she believes that mammograms “do a much better job than I ever could to find a lump.” The nurse should explain to her that:

The mother of a teenage girl calls the gynecology office to ask if it is time to make an appointment for her daughter’s first Pap smear. Which statement by the nurse is most appropriate?