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Solved by verified expert:Assignment:Many of you have experience in complex adaptive systems whether you realize it or not. Thinking about your future practice specialty area (Family Nurse Practitioner), identify an issue or concern common to your future practice setting. (Please use both given references). Discuss how this issue or concern impacts the system at the micro, meso, and macro levels. How will you address this issue or concern at the microsystem level?What is the expected impact of your solution on the meso- and macrosystems? Please use these 2 references. Will attach PDF files with Full Text articles. Please see attached Word document with all intructions


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Many of you have experience in complex adaptive systems whether you realize it
or not. Thinking about your future practice specialty area (Family Nurse
Practitioner), identify an issue or concern common to your future practice
setting. (Please use both given references).

Discuss how this issue or concern impacts the system at the micro, meso,
and macro levels.
How will you address this issue or concern at the microsystem level?
What is the expected impact of your solution on the meso- and
Please use these 2 references. Will attach PDF files with Full Text articles.
Dredze, M., Broniatowski, D. A., Smith, M. C., & Hilyard, K. M. (2016). Understanding
Vaccine Refusal: Why We Need Social Media Now. American Journal of Preventive
Medicine, 50(4), 550–552.
LaSalle, G. (2018). When the answer to vaccines is “No.” The Journal Of Family
Practice, 67(6), 348. Retrieved from
This information from School lesson may be helpful:
Several definitions of healthcare systems exist. The definition of healthcare
systems may vary depending on geographical location and practice setting.
Geographically and on a global scale, the U. S. healthcare system is a mixed
method payer system comprised of Medicare and Medicaid and private payer
systems. In other countries, such as Australia and Canada to name a few, the
healthcare system is universal in which all citizens are provided some equitable
form of access to healthcare whether that country offers it at no cost or on a feebased cost to its citizens (Torabi et al., 2014).
Healthcare systems can also be defined as structures or organizations that
directly or indirectly influence health care through the delivery of services or the
provision of care (Mensik, 2014), including but not limited to hospitals, health
insurance companies, community-based care organizations, academic
institutions, health insurance companies, pharmaceutical companies, technology
companies, and legislative settings. These systems are comprised of
components such as organizations, departments, and units (Mensik, 2014). The
systems can further be classified by levels: mesosystems, macrosystems, and
microsystems. Regardless of specialty area, master’s-prepared advanced
practice nurses work in systems. Understanding the systems and learning skills
and attitudes that can help navigate the system will have a great impact on
practice outcomes.
Systems Theory
Systems theory views a healthcare organization as a dynamic, complex set of
intertwined elements continuously interacting with the environment in which it
operates. A system takes inputs from the open environment in the form of
various energy sources such as money, raw materials, information, and patients.
A system then transforms the inputs via throughput processes and exports the
products into the open environment in the form of outputs.
Throughput occurs when the organization creates a new product, trains staff
members, processes materials, or provides services to patients. These activities
entail some reorganization of input. Systems export some products into the
environment. For healthcare organizations, outputs consist of patients, insurance
reimbursement, staff and patient outcomes, and so forth. The product exported
into the environment provides the sources of energy for the replication of the
cycle of activities (Marquis & Huston, 2017). Systems theory views a healthcare
organization as multidimensional in their assumptions about cause-and-effect
relationships. A change in any element of the healthcare system causes changes
in other elements of the system (Marquis & Huston, 2017). Furthermore, the
pattern of activities of the energy exchanged within healthcare organizations has
a cyclic character. The energy creating the cycle of activities is either derived
from an exchange of the product in the external environment or the activity itself.
Micro-, Meso-, and Macrosystems
Organization systems can be further divided into different levels: micro-, meso-,
and macrosystems. The systems can be viewed as unit, department,
organization, or more globally as department, organization, and community. Each
system level requires new adaptive responses from leaders to create an optimal
practice environment conducive to quality outcomes.
Microsystem: The inner core level represents a department within an
organization such as the school of nursing within a college. In the healthcare
organization, it is represented by patient care units where patients, their
families, and care teams meet to create a collaborative approach to care
delivery. The microsystem is where point-of-care or direct services are
Mesosystem: The second level represents either an organization such as a
college, hospital, or community-based care organization. It can also represent
the major divisions within the healthcare organization such as the department
of nursing, department of medicine, and clinical service programs such as
women’s health programs, oncology, neuroscience, and orthopedics. The
mesosystem is managed by nurse managers and directors.
Macrosystem: This system level is the highest level. This level represents the
community or an entire organization. At the community level, leaders include
government entities, regulatory agencies, and professional organizations. At
the organizational level, leaders include the chief executive officer, president,
chief financial officer, and chief operations officer.
Complex Adaptive Systems
Complex adaptive systems are flexible and fluid in nature. Organizations are
adaptive systems that are integral parts of their environments. They are not
static, but rather, are in constantly shifting states which can create uncertainty
and unpredictability. Complex adaptive systems are learning organizations that
embrace uncertainty and can adapt to emerging change. Master’s prepared
advanced practice nurses must become comfortable with ambiguity and
uncertainty and learn to accept, manage, and benefit from uncertainty which
encourages creativity, innovation, and risk taking (National League for Nursing
[NLN], 2010) that leads to emergence of new order and process within the
organization. Common characteristics of complex adaptive systems include:
parts of systems interact; new behaviors, patterns, and ideas emerge from
relationships; results are nonlinear and unpredictable; and self-organization
occurs with connective leadership and simple rules (Crowell, 2015). From a
complex adaptive system perspective organizations are living systems.
Healthcare and healthcare related organizations must be open and receptive to
the unpredictable, dynamic, and fluid nature of their environments if they are to
Understanding Vaccine Refusal
Why We Need Social Media Now
Mark Dredze, PhD,1 David A. Broniatowski, PhD,2 Michael C. Smith, MS,2 Karen M. Hilyard, PhD3
he recent Disneyland measles outbreak brought
national attention to a growing problem: vaccine
refusal—herd immunity is no longer a reality in
many communities. Only 70% of children aged 19–35
months are up-to-date on immunizations,1 and in some
communities, more than a quarter of school-age children
have exemptions on file (
pdf). Although they vary across the ideological spectrum,
vaccine refusers tend to be well educated, white, and
more affluent than people who typically experience
health disparities.1 Prior studies2,3 have found that a
diversity of motivations drive vaccine refusal, including
fear that vaccines cause autism, concerns over toxins,
beliefs about the benefits of measles to the immune
system, distrust of government, distrust of pharmaceutical companies, and preference for a “natural” lifestyle.
Arguments recommended by physicians’ groups and
public health agencies to counter these beliefs do not
always change minds4; even parents who indicate high
trust in their pediatricians may not follow doctors’
recommendations.1 Ultimately, people “persuade themselves to change attitudes and behavior,”5 and communicators must tailor messages to the beliefs, attitudes, and
motivations of particular audience segments.6 Effective
health communication about vaccines requires answering three questions:
1. How do individuals’ vaccination adherence7 and
vaccine refusal patterns vary with their beliefs? We
cannot assume that rationales for religious exemptions
to vaccination are rooted in the same beliefs as those
driving advocates of “natural cures.”
2. How do beliefs vary by community or social group?
We cannot assume that a liberal Democrat in Los
From the 1Human Language Technology Center of Excellence, Johns
Hopkins University, Baltimore, Maryland; 2Department of Engineering
Management & Systems Engineering, School of Engineering and Applied
Science, George Washington University, Washington, District of Columbia;
and 3Department of Health Promotion and Behavior, College of Public
Health, University of Georgia, Athens, Georgia
Address correspondence to: Mark Dredze, PhD, Human Language
Technology Center of Excellence, Johns Hopkins University, 810 Wyman
Park Drive, Baltimore MD 21211. E-mail: [email protected]
550 Am J Prev Med 2016;50(4):550–552
Angeles refuses vaccinations for the same reasons that
a staunch Texas conservative might.
3. Which persuasive strategies used by vaccination
advocates and vaccine refusers are most effective?
We cannot assume that the same types of arguments
will be compelling to members of different groups.
It is difficult to answer these research questions using
traditional methods,8,9 such as telephone surveys. Traditional survey methods have limitations. A typical phone
survey of 1,000 participants based on random-digit dialing
might cost roughly $70,000. If we want to survey vaccine
refusers, who may be 5% of the general population, we need
to make 20,000 phone calls—a twentyfold increase in cost.
Furthermore, surveys are limited in their ability to track
changes in nuanced beliefs among different populations
over time. Finally, surveys often require months to implement and struggle to deliver timely information, especially
during an outbreak. Traditional methods can fall behind an
emerging public health crisis. We can’t afford to use last
year’s strategies to combat this year’s outbreak.
Enter social media, which provide unprecedented, realtime access to the attitudes, beliefs, and behaviors of
people from across demographic groups. Social media
have increasingly been a hotbed of activity for antivaccination activists.10 In fact, the journal Vaccine devoted
an entire 2012 issue to social media, which they defined as
“websites such as Facebook, Twitter, Wikipedia, LinkedIn
and YouTube.”11 Furthermore, social media can fill in
many gaps left by traditional research methods on vaccine
refusal, and novel computational methods can break new
ground in social media at Big Data scale. Social media
platforms increasingly represent people across age,
income, education, and racial/ethnic groups.12 Twitter,
in particular, has already demonstrated its value for many
areas of public health,13 but there is still vast potential.14
For example, though Twitter remains disproportionately
popular among minorities, the poor, and people aged
younger than 30 years,15 this is a strength: These data
provide critical insight into the attitudes and behaviors of
people who often fall outside the sampling frame provided
by traditional random-digit dialing or address-based
sampling. These same groups are also at greatest risk for
health disparities. Although other major social networks,
such as Facebook, may be more popular among the general
& 2016 American Journal of Preventive Medicine
 Published by Elsevier Inc.
Dredze et al / Am J Prev Med 2016;50(4):550–552
from social media. The messages observed and shared
there can provide a real-time, detailed picture of public
attitudes toward vaccination. For example, though perceived links between autism and the measles, mumps, and
rubella vaccine have persisted for years, concerns about the
presence of putative toxins have gained recent attention.10
Indeed, because the Internet allows such rapid spread of
anti-vaccine arguments,10 it is essential to harness the
strength of the Internet to combat them. Moreover, by
observing these messages directly, researchers can circumvent a common concern about surveys, focus groups, and
interviews—namely, that participants tend to respond in
ways that they believe are expected (“social desirability
bias”), especially in the context of topics like patient
adherence to recommendations and dosages. For sensitive
and controversial topics, such as vaccines, asking people
directly may be less accurate than observing their behavior
on social media. Finally, social media can help us understand which elements of pro-vaccination messages seem to
resonate most with the public and, importantly, how
vaccine refusers persuade others. Effective health communication must combat these anti-vaccination persuasion
campaigns. Similarly, we must understand how the population at large reacts to vaccine refusal. Social media can be
used to track how specific news stories circulate. At the
same time, those who support vaccination often criticize
and deride “anti-vaxxers,” despite decades of research
showing that external attacks can reinforce closely held
beliefs.20 Therefore, we must understand the “how” of both
pro- and anti-vaccine communications. This topic remains
largely unstudied because it is unsupported by traditional
surveys; social media can change that.
The volume of data to support
this research is vast. Consider the
Vaccine Tweets
authors’ analysis of Twitter mes9″
sages, based on statistical natural
language processing, during the re8″
cent measles outbreak (Figure 1).
The authors collected Twitter data
continuously throughout the out5″
break using more than 50 key4″
words associated with vaccines,
such as vaccine, immunization,
and measles. Tweets were then
labeled for relevance to the topic
of vaccines, as well as their
sentiment toward vaccines: positive, negative, or neutral. Three
supervised machine learning clasFigure 1. Tweets expressing positive and negative vaccine attitudes (right y-axis),
and overall vaccine-related tweets (left y-axis), around the January 7, 2015, outbreak.
sifiers (algorithms that can autoNote: Vaccine tweets are normalized using the maximum number of relevant tweets in a
matically categorize text) were
140-day window. Tweets are categorized using statistical natural language processing methods.
trained on these tweets: relevance;
Gaps in the data collection are smoothed by using a surrounding 2-week average for the missing
sentiment bearing (neutral versus
April 2016
population, Twitter’s popularity has overtaken Facebook
among precisely these hard-to-reach groups—namely,
teenagers and young adults.16 Twitter is also one of the
more accessible social media platforms, with fewer privacy
settings ( when compared with
Facebook ( and more fully
public posts or messages, making it an ideal observational
data collection site. Using social media in concert with
traditional research methods, we can better capture the
opinions of vaccine refusers, allowing researchers to find
and investigate public messages broadcast by members of
communities of interest.
Additionally, new computer science research can infer
traditional demographics directly from the data, allowing
alignment between social media and traditional surveys.17,18
Furthermore, computational methods can go beyond traditional demographics, identifying fine-grained cultural
groups such as users of “natural” or “holistic” cures, a
precision that is both costly and time consuming with
typical surveys, yet critical to understanding vaccine refusal.
At least 80% of Internet users seek health information
online, and 16% of those seek online information about
vaccination.10 Although it is not clear how many of these
Internet users specifically seek or incidentally get vaccination information from social media, we do know that
people regularly share vaccine information on social media
platforms, and that the anti-vaccination movement uses
social media as one of its primary communication tools.
Likewise, an important component of social media is the
sharing of news articles and blog posts. Increasing numbers
of people obtain a majority of their news from social media,
including millennials, who get 61% of their political news
Dredze et al / Am J Prev Med 2016;50(4):550–552
opinion); and for those containing an opinion, sentiment
polarity. Using the resulting classifiers, all collected data
were automatically labeled to measure the daily rates of
vaccine tweets and their sentiment. The authors observed
an increase in the number of vaccine-related Twitter
messages during the outbreak, with positive messages
dramatically increasing. These millions of messages
suggest a golden research opportunity.
Social media data are no more valid than any other data
source, but in the case of understanding vaccine refusal,
the strengths of social media data may greatly outweigh
their weaknesses. Importantly, approaches based on social
media complement traditional methods, aligning with the
opinion of top methodologists that the most valid, reliable
research comprises mixed methods and data sources.21
Vaccine refusal is a danger to the public’s health. We
are living in a time when children and other vulnerable
populations are contracting vaccine-preventable illnesses
years after these diseases were thought to have been
eliminated in the developed world. As public opinion and
policy turn against vaccine refusers and drives them into
increasingly vulnerable communities of like-minded
individuals, the situation may only grow worse. To
combat this epidemic, we need additional, and moreeffective, mechanisms for understanding the critical
questions of vaccine refusal. We must turn to social
media now before the next outbreak.
The work presented in this paper is that of the authors and
does not reflect the official policy of NIH. Dredze, Broniatowski, and Hilyard are supported by NIH under award number
1R01GM114771-01. The funders had no role in the study
design; collection, analysis, and interpretation of data; writing
the report; or the decision to submit the report for publication.
The writing was completed by Dredze, Broniatowski, and
Hilyard. Data analysis was completed by Dredze and Smith.
Dredze has received consulting fees from Directing Medicine and Sickweather, companies that use social media for
public health. No other financial disclosures were reported by
the authors of this paper.
1. Bass PF. Vaccine refusal. Contemp Pediatr. 2015;32:7.
2. Zhao Z, Luman E. Progress toward eliminating disparities in vaccination coverage among U.S. children, 2000–2008. Am J Prev Med.
3. Luthy KE, Beckstrand RL, Callister LC, Cahoon S. Reasons parents
exempt children from receiving immunizations. J Sch Nurs. 2012;
4. Nyhan B, Reifler J, Richey S, Freed GL. Effective messages in vaccine promotion: a …
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