Consumer Health Informatics (CHI) and Information Literacy

at
Categories
Tags

 

 

Part 1. Consumer health informatics (CHI) is a field within health informatics devoted to the consumer, or patient, view. It involves computer literacy, health literacy, e-literacy, and digital literacy. CHI focuses on the information structures and processes that empower consumers to manage their own health. CHI also plays a role in healthcare reform because it improves patient engagement and shared decision-making. Patient engagement is contingent upon consumers’ ability to access, understand, and manage personal health care information.

A. Explain how health literacy and eHealth literacy support CHI.

B. Explain the concepts of information literacy, computer literacy, and digital literacy. Why is it important to determine these competencies in patients during patient education? How do these competencies help bridge the digital divide? What are some barriers to closing this divide?

C. Discuss the issues or barriers related to the successful adoption and use of CHI.

Part 2. The number of websites and resources on the Internet is ever increasing. Healthcare professionals must possess the skills to critically evaluate information and guide healthcare consumers to accurate information sources. It is also important for BSN nursing students to be able to identify credible sources!

A. Discuss the attributes of quality information as noted in the Hebda et al. (2019) textbook in Chapter 18. Explain why they are important in CHI and how they support evidence-based nursing practice.

B. Identify ways to evaluate online health information and the credibility of websites and articles.

Reference:
1. Hansdbook of informatics for nurses and healthcare professionals 6th edition 2019 ISBN: 978-0-13-471101-0 Chapters 4, 18, 19
2. Peer reviewed scholarly nursing journal not older than 5 years.

 

 

 

 

 

Sample Solution

Calculate the eigenfaces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated.
3) Calculate the corresponding distribution in M-dimensional weight space for each known individual, by projecting their face images onto the ‘face space’.

After the initialization operations, there are carried out more operations in order to recognize new face images.
4) Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces.
5) Determine if the image is a face at all by checking to see if the image is sufficiently close to ‘face space’.
6) If it is a face, classify the weight pattern as either a known person or as unknown.
7) (Optional) Update the eigenfaces and/or weight patterns.
8) (Optional) If the same unknown face is seen several times, calculate its characteristic weight pattern and incorporate into the known faces[24].

As mentioned earlier, there is a long list of methods that can be used for facial recognition. Four of them, i.e Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method, are the most favorite. Below here, you can find the error rates of those four methods, considered

This question has been answered.

Get Answer
WeCreativez WhatsApp Support
Our customer support team is here to answer your questions. Ask us anything!
👋 Hi, Welcome to Compliant Papers.
Hi, how can I help?