Health Care Delivery Models and Nursing Practic

 

E​‌‍‍‍‌‍‍‌‍‌‌‍‍‍‌‍‌‌‌‍​xamine changes introduced to reform or restructure the U.S. health care delivery system. In a 1,000-1,250 word paper, discuss action taken for reform and restructuring and the role of the nurse within this changing environment. Include the following: Outline a current or emerging health care law or federal regulation introduced to reform or restructure some aspect of the health care delivery system. Describe the effect of this on nursing practice and the nurse’s role and responsibility. Discuss how quality measures and pay for performance affect patient outcomes. Explain how these affect nursing practice and describe the expectations and responsibilities of the nursing role in these situations. Discuss professional nursing leadership and management roles that have arisen and how they are important in responding to​‌‍‍‍‌‍‍‌‍‌‌‍‍‍‌‍‌‌‌‍​ emerging trends and in the promotion of patient safety and quality care in diverse health care settings. Research emerging trends. Predict two ways in which the practice of nursing and nursing roles will grow or transform within the next five years to respond to upcoming trends or predicted issues in health care. You are required to cite to a minimum of three sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the assignment criteria and relevant to nursing practice. Prepare this assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successfu​‌‍‍‍‌‍‍‌‍‌‌‍‍‍‌‍‌‌‌‍​l

 

Sample Solution

Visual descriptors
To match the available dataset, visual descriptions from the FC7 layer of the AlexNet convolutional neural network will be used. These represent abstract, top-level features that are discovered in each key frame, and are descriptors of color and texture.

3.3 Training process
The datasets that will be used for the training of the recommendation system are called MMTF-14K (Deldjoo), MovieLens 20M (reference), and UC Irvine Machine Learning Lab’s Movie Data Set, which has data on the cast of over 10,000 movies.

3.4 Summarization
During the summarization process, video segments are ranked based on computed similarity measures between the user profile and the movie features. Personalized movie summarization can be seen as “the process of measuring the similarity score of each video segment for the given user preferences and selecting those top ranked segments that will increase the cumulative similarity score of the summary” (Kannan et al., 2015).
First, the similarity between each shot and the user preferences on actor appearance, genre, and visual descriptors is calculated using cosine similarity measures. Each shot is stored as a vector of its features in a high-dimensional space, after which the angles between the vectors are calculated as the cosine similarity between the vectors. After this, user profiles are created based on their ratings on the same features on movies and the similarity between a shot and a user is computed similarly. This should return a ranked list of shots to select for that specific user.

3.5 Evaluation
In accordance with previous studies on automatically generated movie trailers, a qualitative user study will be performed to evaluate the summarization system. This presents the “cold-start” problem of recommendation, as there will be no data on the users in question. To alleviate this problem, the most direct way is to make a rapid profile of a new user by asking for explicit ratings after presenting a number of movies to the user.
In an online questionnaire format, 20-50 users will first be given 20 movies to rate, afte

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