NURS 6050 Discussion Big Data Risks and Rewards Walden

NURS 6050 Discussion Big Data Risks and Rewards Walden

NURS 6050 Discussion Big Data Risks and Rewards Walden

NURS 6050 Discussion Big Data Risks and Rewards Walden

When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.

From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.

Online Nursing Essays

Struggling to Meet Your Deadline?

Get your assignment on NURS 6050 Discussion Big Data Risks and Rewards Walden done on time by medical experts. Don’t wait – ORDER NOW!

To Prepare:

  • Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
  • Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.

By Day 3 of Week 5

Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.

Great insight on how to blend using big data strategically modified to be applicable with considering barriers. It sounds like the strategies you’ve identified align with using data in a valuable and meaningful way. McGonigle & Mastrian, 2022, state, “The data in big clinical datasets can get lost, diminishing their value. Therefore, it is imperative that KDD and AI be used to analyze these datasets to discover meaningful information that will influence healthcare practice” (p. 558). This was a great example of how technology was a facilitator.

Facilitators and driving forces that keep technology use well-received by nursing can include knowledge and skills in evidence-based practice (McGonigle & Mastrian, 2022). These alerts are helping and influencing health care practice by nurses using this technology to provide safe quality care in the setting you describe clinically. You also mentioned alert fatigue, and the research indicates a strategy also to reduce alert fatigue is by attracting the attention of patients and clinicians instead of solely reducing the total number of alerts (Wan et al., 2020). Expanding the use of alert reminders to the patients themselves would take this one step further. Mobile alerts on patients’ cell phones can promote positive health behavior change, adherence to health regimes, and exposure to educational health information (Perri-Moore et al., 2016). I think nurses will learn how to adapt to a revolution of changes happening now and only more to come.

References

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning

Perri-Moore, S., Kapsandoy, S., Doyon, K., Hill, B., Archer, M., Shane-McWhorter, L., Bray, B. E., & Zeng-Treitler, Q. (2016). Automated alerts and reminders targeting patients: A review of the literature. Patient Education and Counseling99(6), 953-959. https://doi.org/10.1016/j.pec.2015.12.010

Wan, P. K., Satybaldy, A., Huang, L., Holtskog, H., & Nowostawski, M. (2020). Reducing alert fatigue by sharing low-level alerts with patients and enhancing collaborative decision making using Blockchain technology: Scoping review and proposed framework (MedAlert). Journal of medical Internet research22(10), e22013.

By Day 6 of Week 5

Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.

Big data analytics that is evolved from business intelligence and decision support systems enable healthcare organizations to analyze an immense volume, variety and velocity of data across a wide range of healthcare networks to support evidence-based decision making and action taking (Wang et.al,2018). Big data analytics encompasses the various analytical techniques such as descriptive analytics and mining/predictive analytics that are ideal for analyzing a large proportion of text-based health documents and other unstructured clinical data (Groves et al., 2013

Links to an external site.). Big data in healthcare is a term used to describe a large amount of information created by digital technologies that collect patient information and can help manage a hospitals overall performance. One potential benefit of using big data as part of a clinical system is by improved patient staffing which in turn can improve patient outcomes. Many staff managers at hospitals must predict for either a low or high influx in patient at any given day to determine how staffed a facility will be.

A fully staffed unit will a low amount of patients will indeed improve patient outcomes but will also create unnecessary labor cost for the unit. On the other hand, a unit with many patients and decrease staff can cause an increase in patient complaints, deaths, and chances of medication errors. With the use of big data, an algorithm can be created to gather all data of patient records and hospital admission from previous years to analyze the need for staff on any given day according to previous data collected.

One potential challenge of using big data as part of a clinical system is the lack of appropriate skills. It is important that health care workers are also kept up to date with the use of constantly changing technology, techniques, and a constantly moving standard of care. Due to the constant evolution of technology, there exist populations of individuals lacking specific skills; as such this is also a significant continuing barrier to the implementation of big data (Kruse et. al,2023).

One potential strategy I have observed that is effective with the many challenges of big data, is the appropriate training. With technology challenging and the use of big data being integrated into healthcare, the most effective way to adapt to the change is through effective training. The many opportunities to improve patient care won’t be effective unless the person using the software is well trained.

References:

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizationsLinks to an external site.

Links to an external site.Technological Forecasting and Social Change, 126(1), 3–13. 

Groves, P., Knott, D., Kuiken, S. V., & Kayyali, B.,. (2013, April 1). The Big-Data Revolution in US health care: Accelerating value and innovation. McKinsey & Company. Retrieved March 31, 2023, from https://www.mckinsey.com/industries/healthcare/our-insights/the-big-data-revolution-in-us-health-care 

Kruse, C., Goswamy , R., Raval, Y., & Marawi, S. (n.d.). Challenges and opportunities of Big Data in health care: A systematic review. JMIR medical informatics. Retrieved March 31, 2023, from https://pubmed.ncbi.nlm.nih.gov/27872036/ 

*Note: Throughout this program, your fellow students are referred to as colleagues.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 5 Discussion Rubric

Post by Day 3 and Respond by Day 6 of Week 5

To participate in this Discussion:

Week 5 Discussion

Next Module

To go to the next module:

Module 4

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: NURS 6050 Discussion Big Data Risks and Rewards Walden

Module 3: Data-Information-Knowledge-Wisdom (DIKW) (Week 5)

Laureate Education (Producer). (2018). Data-Information-Knowledge-Wisdom [Video file]. Baltimore, MD: Author.

Learning Objectives

Students will:

  • Analyze benefits, challenges, and risks of using big data in clinical systems
  • Recommend strategies to mitigate challenges and risks of using big data in clinical systems
Due By Assignment
Week 5, Days 1–2 Read/Watch/Listen to the Learning Resources.
Compose your initial Discussion post.
Week 5, Day 3 Post your initial Discussion post.
Week 5, Days 4-5 Review peer Discussion posts.
Compose your peer Discussion responses.
Week 5, Day 6 Post at least two peer Discussion responses on two different days (and not the same day as the initial post).
Week 5, Day 7 Wrap up Discussion.

Learning Resources

Required Readings

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.

  • Chapter 22, “Data Mining as a Research Tool” (pp. 477-493)
  • Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology” (pp. 537-551)

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47. Retrieved from https://www.americannursetoday.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf

Thew, J. (2016, April 19). Big data means big potential, challenges for nurse execs. Retrieved from https://www.healthleadersmedia.com/nursing/big-data-means-big-potential-challenges-nurse-execs

Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13. 

Required Media

Laureate Education (Executive Producer). (2012). Data, information, knowledge and wisdom continuum [Multimedia file]. Baltimore, MD: Author. Retrieved from http://mym.cdn.laureate-media.com/2dett4d/Walden/NURS/6051/03/mm/continuum/index.html

Laureate Education (Producer). (2018). Health Informatics and Population Health: Analyzing Data for Clinical Success [Video file]. Baltimore, MD: Author.

Vinay Shanthagiri. (2014). Big Data in Health Informatics [Video file]. Retrieved from https://www.youtube.com/watch?v=4W6zGmH_pOw

Rubric Detail

Select Grid View or List View to change the rubric’s layout.
Content
Name: NURS_5051_Module03_Week05_Discussion_Rubric

Grid View
List View

Excellent Good Fair Poor
Main Posting

Points Range: 45 (45%) – 50 (50%)

Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources.

Supported by at least three current, credible sources.

Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

Points Range: 40 (40%) – 44 (44%)

Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module.

At least 75% of post has exceptional depth and breadth.

Supported by at least three credible sources.

Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

Points Range: 35 (35%) – 39 (39%)

Responds to some of the discussion question(s).

One or two criteria are not addressed or are superficially addressed.

Is somewhat lacking reflection and critical analysis and synthesis.

Somewhat represents knowledge gained from the course readings for the module.

Post is cited with two credible sources.

Written somewhat concisely; may contain more than two spelling or grammatical errors.

Contains some APA formatting errors.

Points Range: 0 (0%) – 34 (34%)

Does not respond to the discussion question(s) adequately.

Lacks depth or superficially addresses criteria.

Lacks reflection and critical analysis and synthesis.

Does not represent knowledge gained from the course readings for the module.

Contains only one or no credible sources.

Not written clearly or concisely.

Contains more than two spelling or grammatical errors.

Does not adhere to current APA manual writing rules and style.
Main Post: Timeliness
Points Range: 10 (10%) – 10 (10%)
Posts main post by day 3.

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)
Does not post by day 3.
First Response

Points Range: 17 (17%) – 18 (18%)

Response exhibits synthesis, critical thinking, and application to practice settings.

Responds fully to questions posed by faculty.

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

Demonstrates synthesis and understanding of learning objectives.

Communication is professional and respectful to colleagues.

Responses to faculty questions are fully answered, if posed.

Response is effectively written in standard, edited English.

Points Range: 15 (15%) – 16 (16%)

Response exhibits critical thinking and application to practice settings.

Communication is professional and respectful to colleagues.

Responses to faculty questions are answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

Points Range: 13 (13%) – 14 (14%)

Response is on topic and may have some depth.

Responses posted in the discussion may lack effective professional communication.

Responses to faculty questions are somewhat answered, if posed.

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

Points Range: 0 (0%) – 12 (12%)

Response may not be on topic and lacks depth.

Responses posted in the discussion lack effective professional communication.

Responses to faculty questions are missing.

No credible sources are cited.
Second Response

Points Range: 16 (16%) – 17 (17%)

Response exhibits synthesis, critical thinking, and application to practice settings.

Responds fully to questions posed by faculty.

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

Demonstrates synthesis and understanding of learning objectives.

Communication is professional and respectful to colleagues.

Responses to faculty questions are fully answered, if posed.

Response is effectively written in standard, edited English.

Points Range: 14 (14%) – 15 (15%)

Response exhibits critical thinking and application to practice settings.

Communication is professional and respectful to colleagues.

Responses to faculty questions are answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

NURS 6050 Discussion Big Data Risks and Rewards Walden

Response is effectively written in standard, edited English.

Points Range: 12 (12%) – 13 (13%)

Response is on topic and may have some depth.

Responses posted in the discussion may lack effective professional communication.

Responses to faculty questions are somewhat answered, if posed.

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

Points Range: 0 (0%) – 11 (11%)

Response may not be on topic and lacks depth.

Responses posted in the discussion lack effective professional communication.

Responses to faculty questions are missing.

No credible sources are cited.
Participation

Points Range: 5 (5%) – 5 (5%)

Meets requirements for participation by posting on three different days.

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)

Over the past decade, there has been a greater emphasis on the involvement of registered nurses in the development and implementation of health information technology systems to maintain patient safety and improve the quality of care services. Today, electronic health records remain a great source of protected health information and clinical documentation during the provision of care services by registered nurses and other healthcare professionals (Reid et al., 2021). The rapid deployment of EHR by healthcare organizations has created room for registered nurses to create digital versions of patient medical records and transform them into valuable clinical knowledge for preventing adverse events like patient falls and nosocomial infections, among many others.

One of the greatest risks of utilizing big data from the digital versions of patient medical records is to maintain the integrity and quality of information system output (McGonigle & Mastrian, 2022). For instance, the digital versions of patient medical records are prone to manipulation and misinterpretation due to weak information security measures and the lack of relevant knowledge and skills for maintaining data integrity and quality. Through regular education and training, registered nurses and other healthcare professionals develop the required nursing informatics competencies, like maintaining strong access credentials for clinical information systems and data encryption to prevent manipulation and unauthorized access.

References

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Reid, L., Maeder, A., Button, D., Breaden, K., & Brommeyer, M. (2021). Defining nursing informatics: A narrative review. Studies in Health Technology and Informatics284, 108–112. https://doi.org/10.3233/SHTI210680

Name: NURS_5051_Module03_Week05_Discussion_Rubric

  Excellent Good Fair Poor
Main Posting
 
Points Range: 45 (45%) – 50 (50%)

Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources.

Supported by at least three current, credible sources.

Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

 
Points Range: 40 (40%) – 44 (44%)

Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module.

At least 75% of post has exceptional depth and breadth.

Supported by at least three credible sources.

Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

 
Points Range: 35 (35%) – 39 (39%)

Responds to some of the discussion question(s).

One or two criteria are not addressed or are superficially addressed.

Is somewhat lacking reflection and critical analysis and synthesis.

Somewhat represents knowledge gained from the course readings for the module.

Post is cited with two credible sources.

Written somewhat concisely; may contain more than two spelling or grammatical errors.

Contains some APA formatting errors.

 
Points Range: 0 (0%) – 34 (34%)

Does not respond to the discussion question(s) adequately.

Lacks depth or superficially addresses criteria.

Lacks reflection and critical analysis and synthesis.

Does not represent knowledge gained from the course readings for the module.

Contains only one or no credible sources.

Not written clearly or concisely.

Contains more than two spelling or grammatical errors.

Does not adhere to current APA manual writing rules and style.

Main Post: Timeliness
 
Points Range: 10 (10%) – 10 (10%)
Posts main post by day 3.
 
Points Range: 0 (0%) – 0 (0%)
 
Points Range: 0 (0%) – 0 (0%)
 
Points Range: 0 (0%) – 0 (0%)
Does not post by day 3.
First Response
 
Points Range: 17 (17%) – 18 (18%)

Response exhibits synthesis, critical thinking, and application to practice settings.

Responds fully to questions posed by faculty.

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

Demonstrates synthesis and understanding of learning objectives.

Communication is professional and respectful to colleagues.

Responses to faculty questions are fully answered, if posed.

Response is effectively written in standard, edited English.

 
Points Range: 15 (15%) – 16 (16%)

Response exhibits critical thinking and application to practice settings.

Communication is professional and respectful to colleagues.

Responses to faculty questions are answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

 
Points Range: 13 (13%) – 14 (14%)

Response is on topic and may have some depth.

Responses posted in the discussion may lack effective professional communication.

Responses to faculty questions are somewhat answered, if posed.

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

 
Points Range: 0 (0%) – 12 (12%)

Response may not be on topic and lacks depth.

Responses posted in the discussion lack effective professional communication.

Responses to faculty questions are missing.

No credible sources are cited.

Second Response
 
Points Range: 16 (16%) – 17 (17%)

Response exhibits synthesis, critical thinking, and application to practice settings.

Responds fully to questions posed by faculty.

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

Demonstrates synthesis and understanding of learning objectives.

Communication is professional and respectful to colleagues.

Responses to faculty questions are fully answered, if posed.

Response is effectively written in standard, edited English.

 
Points Range: 14 (14%) – 15 (15%)

Response exhibits critical thinking and application to practice settings.

Communication is professional and respectful to colleagues.

Responses to faculty questions are answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

 
Points Range: 12 (12%) – 13 (13%)

Response is on topic and may have some depth.

Responses posted in the discussion may lack effective professional communication.

Responses to faculty questions are somewhat answered, if posed.

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

 
Points Range: 0 (0%) – 11 (11%)

Response may not be on topic and lacks depth.

Responses posted in the discussion lack effective professional communication.

Responses to faculty questions are missing.

No credible sources are cited.

Participation
 
Points Range: 5 (5%) – 5 (5%)
Meets requirements for participation by posting on three different days.
 
Points Range: 0 (0%) – 0 (0%)
 
Points Range: 0 (0%) – 0 (0%)
 
Points Range: 0 (0%) – 0 (0%)
Does not meet requirements for participation by posting on 3 different days.
Total Points: 100

BIG DATA RISKS AND REWARDS

Big data allow you to see the larger picture of your patient, especially if you are working in the emergency room [ER]. It is very challenging. You must have seen a lot of cases. When you get your patient history and assess them in totality, it will give you a more significant idea or picture of their problem.
Big data allow nurses and other healthcare professionals to deliver the best care possible. It can be used by various parties in hospitals or nursing homes, further reducing the expense of medical errors while promoting safe practice.
Data is a crucial driving force for organizational change and new developments. The more information a healthcare organization has, the more it can organize itself to deliver the best healthcare services to its clients. Therefore, big data in the healthcare sector refers to vast volumes of data generated from adopting digital technologies and interactions between healthcare stakeholders and healthcare systems in collecting, documenting, and retrieving healthcare data (Wang et al., 2018).
From research studies, government agencies, and laboratory results, healthcare personnel can collect big data; all these have proved to help manage organizational performance. one of the main advantages of using big data in the healthcare industry is the ability to anticipate future trends and events of specific parameters, which would then serve as actionable information that serves as the foundation for evidence-based interventions. (Wang et al., 2018). Big data, for instance, could assist a company in forecasting future trends in the prevalence of lifestyle diseases among a specific community. The company could use the prediction to develop evidence-based interventions targeting cost-cutting, value-based care, and higher service quality.

BIG DATA RISKS AND REWARDS

When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.

From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.

As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.

RESOURCES

Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources. 

WEEKLY RESOURCES

To Prepare:

  • Review the Resources and reflect on the web article Big Data Means Big Potential, Challenges for Nurse Execs.
  • Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.

BY DAY 3 OF WEEK 5

Post a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.

BY DAY 6 OF WEEK 5

Respond to at least two of your colleagues* on two different days, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.

Firstly, your post was informative and interesting. You are right! A challenge of using big data in healthcare organizations is system failure. On the article, Evidence-based Contingency Planning for Electronic Health Record Downtime (n.d.), describes EHR failure as, “It may be caused by system failures; software or hardware issues; interruptions to internet connectivity; or catastrophic events, such as natural disasters and terrorism, including cyber-attacks. 

In my organization for example, there have been instances where system failure occurred.  When this happens, all electronic operations are halted.  Larsen et al., (2019) describes, “Electronic health record (EHR) downtime is any period during which the EHR system is fully or partially unavailable”. Nurses like me, most times find it difficult to perform our duties. The combined analysis of performance data and interviews performed on this study by Larsen et al., (2019) concluded that downtime poses unique and significant threat on healthcare organizations. Some healthcare organizations such as mine, have implemented alternatives measures such as “downtime mode” should such problems arise. I believe that having an organized system in place for employees to operated manually is one way to address this problem. Healthcare organizations who have transitioned from paper to EHR must have employees trained on alternatives approaches to electronic systems. 

*Note: Throughout this program, your fellow students are referred to as colleagues.

NURS_5051_Module03_Week05_Discussion_Rubric

NURS_5051_Module03_Week05_Discussion_Rubric
CriteriaRatingsPts
This criterion is linked to a Learning OutcomeMain Posting50 to >44.0 pts Excellent Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources. … Supported by at least three current, credible sources. … Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style. 44 to >39.0 pts Good Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module. … At least 75% of post has exceptional depth and breadth. … Supported by at least three credible sources. … Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style. 39 to >34.0 pts Fair Responds to some of the discussion question(s). … One or two criteria are not addressed or are superficially addressed. … Is somewhat lacking reflection and critical analysis and synthesis. … Somewhat represents knowledge gained from the course readings for the module. … Post is cited with two credible sources. … Written somewhat concisely; may contain more than two spelling or grammatical errors. … Contains some APA formatting errors. 34 to >0 pts Poor Does not respond to the discussion question(s) adequately. … Lacks depth or superficially addresses criteria. … Lacks reflection and critical analysis and synthesis. … Does not represent knowledge gained from the course readings for the module. … Contains only one or no credible sources. … Not written clearly or concisely. … Contains more than two spelling or grammatical errors. … Does not adhere to current APA manual writing rules and style.50 pts
This criterion is linked to a Learning OutcomeMain Post: Timeliness10 to >0.0 pts Excellent Posts main post by day 3. 0 pts Poor Does not post by day 3.10 pts
This criterion is linked to a Learning OutcomeFirst Response18 to >16.0 pts Excellent Response exhibits synthesis, critical thinking, and application to practice settings. … Responds fully to questions posed by faculty. … Provides clear, concise opinions and ideas that are supported by at least two scholarly sources. … Demonstrates synthesis and understanding of learning objectives. … Communication is professional and respectful to colleagues. … Responses to faculty questions are fully answered, if posed. … Response is effectively written in standard, edited English. 16 to >14.0 pts Good Response exhibits critical thinking and application to practice settings. … Communication is professional and respectful to colleagues. … Responses to faculty questions are answered, if posed. … Provides clear, concise opinions and ideas that are supported by two or more credible sources. … Response is effectively written in standard, edited English. 14 to >12.0 pts Fair Response is on topic and may have some depth. … Responses posted in the discussion may lack effective professional communication. … Responses to faculty questions are somewhat answered, if posed. … Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited. 12 to >0 pts Poor Response may not be on topic and lacks depth. … Responses posted in the discussion lack effective professional communication. … Responses to faculty questions are missing. … No credible sources are cited.18 pts
This criterion is linked to a Learning OutcomeSecond Response17 to >15.0 pts Excellent Response exhibits synthesis, critical thinking, and application to practice settings. … Responds fully to questions posed by faculty. … Provides clear, concise opinions and ideas that are supported by at least two scholarly sources. … Demonstrates synthesis and understanding of learning objectives. … Communication is professional and respectful to colleagues. … Responses to faculty questions are fully answered, if posed. … Response is effectively written in standard, edited English. 15 to >13.0 pts Good Response exhibits critical thinking and application to practice settings. … Communication is professional and respectful to colleagues. … Responses to faculty questions are answered, if posed. … Provides clear, concise opinions and ideas that are supported by two or more credible sources. … Response is effectively written in standard, edited English. 13 to >11.0 pts Fair Response is on topic and may have some depth. … Responses posted in the discussion may lack effective professional communication. … Responses to faculty questions are somewhat answered, if posed. … Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited. 11 to >0 pts Poor Response may not be on topic and lacks depth. … Responses posted in the discussion lack effective professional communication. … Responses to faculty questions are missing. … No credible sources are cited.17 pts
This criterion is linked to a Learning OutcomeParticipation5 to >0.0 pts Excellent Meets requirements for participation by posting on three different days. 0 pts Poor Does not meet requirements for participation by posting on 3 different days.5 pts
Total Points: 100

Don’t wait until the last minute

Fill in your requirements and let our experts deliver your work asap.