10/31/22, 1:34 PM Role Delineation

https://barry.instructure.com/courses/1612798/assignments/7073264 1/1

Total Points: 100

Role Delineation

Due No Due Date Points 100 Submitting a file upload (Turnitin enabled)

Role Delineation

Criteria Ratings Pts

25 pts

20 pts

20 pts

20 pts

5 pts

5 pts

5 pts

Start Assignment

The role delineation section allows the reader to identify how you will actualize each competency outlined by the National Organization of Nurse Practitioner Faculty (NONPF) and the AACN. It is as unique as each student. You may write a narrative and include supporting documents to highlight your activities. Ultimately it shows the progression towards the program goals.

Essential Components of Role Delineation

Examples /Supporting Evidence

Role Delineation based on NP Competencies

Narrative format plus evidence is required

Management of Patient Health/Illness Status

Reflections on class lectures, discussions, clinical experiences, readings, assignments

NP-Pt Relationship Reflections on class lectures, discussions, clinical experiences readings, assignments

Teaching-Coaching Function

Sample of outline of teaching rounds, poster, in- service etc

Professional Role May be evident in C-V, ,Proof of attendance at BON meeting

Negotiating HC Delivery Systems

Attendance at Lambda Chi Meeting, Pri-Med, CMS seminar

Monitoring/ Ensuring Quality of HC

Sample of peer reviews, quality improvement activities, Magnet offerings

Cultural Competence Article citation, Web References

Managment of Patient Health/Illness Status 25 pts Full Marks

0 pts No Marks

NP-PT Relationship 20 pts Full Marks

0 pts No Marks

Teaching-Coaching Function 20 pts Full Marks

0 pts No Marks

Professional Role 20 pts Full Marks

0 pts No Marks

Negotiating HC Delivery Systems 5 pts Full Marks

0 pts No Marks

Monitoring/ Ensuring Quality of HC 5 pts Full Marks

0 pts No Marks

Cultural Competence 5 pts Full Marks

0 pts No Marks

classification

March 24, 2022

[1]: import numpy as np from sklearn.naive_bayes import GaussianNB

[2]: X = np.genfromtxt('iris.csv', delimiter=',', skip_header=0)

[3]: X.shape

[3]: (150, 5)

[4]: # Shuffle X so that the classes are more uniformly distributed. np.random.seed(12) # having the same seed allows to always generate the same␣ ↪→pseudorandom numbers.

np.random.shuffle(X)

[5]: # Divide X into a training set (100 rows) and a test set (50 rows). X_train = X[:100, :4] # training set, features y_train = X[:100, 4] # training set, targets X_test = X[100:, :4] # test set, features y_test = X[100:, 4] # test set, targets

[6]: # The training set is used to train a model that will classify new examples. # The function fit takes two parameters: the features ( or attributes) and the␣ ↪→target.

model = GaussianNB().fit(X_train, y_train)

[7]: # Apply new test cases to the model, and store the predictions in a variable␣ ↪→y_pred.

y_pred = model.predict(X_test)

[8]: # Find the accuracy of the model # the accuracy of the model is found by counting the number of correct␣ ↪→predictions.

[9]: np.sum(y_pred == y_test)

[9]: 47

1

[10]: # accuracy in percentage np.sum(y_pred == y_test) / len(y_test) * 100

[10]: 94.0

[ ]:

2

(a) Read the notes on classification.

(b) Modify the classification code to run the same analysis but on the data set  titanic.csv Download titanic.csv .

Some information on the data set: - data on the Titanic disaster - The goal is to predict if a given person survived or not the Titanic disaster. - each row represents one person (passenger) on the Titanic - target: survived (0), did not survive (1). This is the first column. - features: Pclass, Sex, Age, Siblings/Spouses Aboard, Parents/Children Aboard, Fare

Use the same classification algorithm GaussianNB, and split the data set into 700 rows for training, and the rest for testing. Use a random seed equal to 12.

(c) Repeat the previous step, but use the following classification algorithms. You can learn how to use these classifiers on the scikit-learn website.

KNeighborsClassifier RandomForestClassifier ExtraTreesClassifier SVC

(d) Upload your entire code to Canvas in a Python file, i.e. the extension of the file should be py.

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