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WEEK 7 MEDICAL SW DQ AND PEER RESPONSES
MEDICAL SOCIAL WORK AND TEAM
WORK
Effective health care teams do not result from a random grouping of professionals.
Instead, they are deliberately constructed to meet the biopsychosocial needs of patients.
Providers who might be found on a health care team include physicians, nurses, social
workers, physical therapists, occupational therapists, and home care workers, among
others. Members of health care teams can work in the same health care setting, or they
can collaborate across settings.
Social workers are integral members of health care teams, given their specialized skills
and their ability to see patients and their illnesses in multidimensional contexts. In this
Discussion, you analyze teams, the social work role, and potential challenges in
interdisciplinary practice.
RESOURCES
Be sure to review the Learning Resources before completing this activity.
Readings
• Gehlert, S., & Browne, T. (Eds.). (2019). Handbook of health social work (3rd ed.).
Wiley.
To prepare:
•
•
Review the Learning Resources on interdisciplinary and integrated teams in
healthcare. As you read, consider the various types of teams in healthcare
settings and what makes an interdisciplinary team effective.
Reflect on the medical social worker’s role on such teams and the challenges
that might arise.
BY DAY 3
Post a brief explanation of the philosophy behind teamwork in healthcare settings. Briefly
describe different types of teams and their functions. Then, explain the essential
requirements for effective interdisciplinary practice. Examine the challenges involved in
interdisciplinary practice, focusing on the role of the medical social worker in particular.
BY DAY 6
Respond to at least two colleagues:
•
•
Identify and share additional challenges your colleague might encounter when
working with a team.
Explain strategies you would employ to address the challenges.
Use the Learning Resources to support your posts. Make sure to provide APA citations
and a reference list.
o
Chapter 9, “The Implementation of Integrated Behavioral Health
Models” (pp. 189–208)
• Craig, S. L., Eaton, A. D., Belitzky, M., Kates, L. E., Dimitropoulos, G., & Tobin, J.
(2020). Empowering the team: A social work model of interprofessional
collaboration in hospitals.Links to an external site. Journal of Interprofessional
Education & Practice, 19. https://doi.org/10.1016/j.xjep.2020.100327
• James, T. A. (2021, August 6). Teamwork as a core value in health care. Links to an
external site.https://postgraduateeducation.hms.harvard.edu/trendsmedicine/teamwork-core-value-health-care
• Marmo, S., & Berkman, C. (2020). Hospice social workers’ perception of being
valued by the interdisciplinary team and the association with job
satisfaction.Links to an external site. Social Work in Health Care, 59(4), 219–235.
https://doi.org/10.1080/00981389.2020.1737306
• Weng, S. S., & Valenzuela, J. (2022).Working with older adults in integrated health
care: Social workers’ perspective.Links to an external site. Journal of Applied
Gerontology, 41(10), 2235–2243. https://doi.org/10.1177/07334648221105266
• Zerden, L. D., Lombardi, B. M., & Richman, E. L. (2019). Social workers on the
interprofessional integrated team: Elements of team integration and barriers to
practice.Links to an external site. Journal of Interprofessional Education & Practice,
17. https://doi.org/10.1016/j.xjep.2019.100286
• National Association of Social Workers. (2016). NASW standards for social work
practice in health care settings.Links to an external
site. https://www.socialworkers.org/LinkClick.aspx?fileticket=fFnsRHX4HE%3d&portalid=0
o See Standard 8, “Interdisciplinary and Interorganizational
Collaboration.”
Peer Chelsie
Post a brief explanation of the philosophy behind teamwork in healthcare settings.
Briefly describe different types of teams and their functions.
From what I gathered the philosophy behind teamwork in a healthcare setting is to ensure
the proper services are being provided. It was identified by the National Association of
Social Workers that Social workers should be competent in different teamwork models
that are common in health care settings, including multidisciplinary models of different
disciplines working together, each drawing on their knowledge such as interdisciplinary
models (different disciplines working in a coordinated fashion toward a common goal for
the client); and transdisciplinary models (a team of health care professionals cooperating
across disciplines to improve patient care through practice or research) (NASW., 2016).
The gathered information explained that these models assist the team with developing a
plan for the members in a collaborative way.
Then, explain the essential requirements for effective interdisciplinary practice.
Examine the challenges involved in interdisciplinary practice, focusing on the role of the
medical social worker in particular.
I believe that one challenge that is involved in interdisciplinary practice will be the lack of
space to conduct your work according to Gehlert and Browne Physical space was also a
reported barrier, with more than a quarter of respondents indicating their work setting
had no designated office space for the social worker. Relatedly, the lack of computer
access and availability of technology affected social work practice, with less than twothirds of social workers reporting they routinely entered information into patients’ EHR
for all team members to review (Gehlert&Browne ., 2019). I used to work at a psychiatric
hospital, and we were not provided with a designated office space. While working in this
setting we also had to have the proper documents with us as well, this became
overwhelming as well. Nancarrow et al., 2013 explained that some characteristics are
positive leadership and management attributes; communication strategies and structures;
personal rewards, training, and development; appropriate resources and procedures;
appropriate skill mix; supportive team climate; and individual (Nancarrow et al.,2013). I
also believe that social workers must have an open line of communication and be open to
different strategies.
References:
Gehlert, S., & Browne, T. (Eds.). (2019). Handbook of health social work (3rd ed.). Wiley.
National Association of Social Workers. (2016). NASW standards for social work practice in
health care settings.Links to an external site.Links to an external
site. https://www.socialworkers.org/LinkClick.aspx?fileticket=fFnsRHX4HE%3d&portalid=0Links to an external site.
Nancarrow SA, Booth A, Ariss S, Smith T, Enderby P, Roots A. Ten principles of good
interdisciplinary team work. Hum Resour Health. 2013 May 10;11:19. doi: 10.1186/14784491-11-19. PMID: 23663329; PMCID:
PMC3662612 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3662612/#:~:text=Ten%
20characteristics%20underpinning%20effective%20interdisciplinary,mix%3B%20suppo
rtive%20team%20climate%3B%20individual
Peer Augustine
Philosophy behind teamwork.
Teamwork in healthcare settings is guided by the philosophy that collaboration among
various healthcare professionals leads to improved patient outcomes, enhanced quality of
care, and a more comprehensive approach to health (James, 2021). This philosophy
recognizes that no single healthcare professional can address all aspects of a patient’s
well-being and that a collective effort is essential for providing holistic and effective care.
Types of teams and their functions.
The National Association of Social Workers (2016) identifies three types of teams. First,
interdisciplinary teams bring together individuals with diverse backgrounds, skills, and
expertise to collaborate on complex problems, such as doctors, nurses, pharmacists,
therapists, and social workers, working together to achieve a common goal. Their primary
function is cooperating and integrating their expertise to address complex patient needs
(Gehlert & Browne, 2019). Second, multidisciplinary teams, like interdisciplinary teams,
involve professionals from different disciplines. However, in a multidisciplinary team, each
member works within their discipline and provides input based on their specialized
knowledge (National Association of Social Workers, 2016). While communication and
coordination are still important, collaboration may not be as integrated as in
interdisciplinary teams. Lastly, transdisciplinary teams take collaboration a step further
by involving professionals from different disciplines and breaking down the traditional
boundaries between disciplines. In a transdisciplinary approach, team members work
collaboratively to integrate their expertise in a way that transcends disciplinary
perspectives, aiming for a unified understanding and solution to complex problems
(National Association of Social Workers, 2016).
Essential requirements.
Effective interdisciplinary practice in healthcare necessitates open and effective
communication among team members, facilitating the exchange of crucial information,
discussion of treatment plans, and coordination of patient care (Gehlert & Browne, 2019).
However, clear, shared goals and objectives aligning with optimal patient care are vital for
guiding the team’s efforts (Craig et al., 2020). Additionally, a foundation of respect and
trust is essential, with team members valuing each other’s expertise and demonstrating
commitment to the common goal of patient well-being. James (2021) maintains that role
clarity is imperative for effective interdisciplinary practice, ensuring that each team
member comprehensively understands their responsibilities to enhance collaboration.
Challenges.
Interdisciplinary practice in healthcare, particularly involving medical social workers,
confronts several challenges. For example, communication barriers pose a significant
obstacle, restricting the exchange of crucial information among healthcare professionals
and consequently impacting the overall quality of patient care (James, 2021). Differing
perspectives on culture and terminology among disciplines can lead to conflicts in
decision-making, and role ambiguity may result in duplicated efforts or overlooked tasks
and too many caseloads (Zerden et al., 2019). Time constraints in busy healthcare settings
pose challenges for coordinating collaborative team meetings and discussing patient care.
Furthermore, Zerden et al., (2019) note that hierarchical structures within healthcare
organizations can introduce power dynamics issues, potentially affecting team members’
willingness to express their opinions or voice concerns openly, including medical social
workers.
References
Craig, S. L., Eaton, A. D., Belitzky, M., Kates, L. E., Dimitropoulos, G., & Tobin, J. (2020).
Empowering the team: A social work model of interprofessional collaboration in
hospitals. Journal of Interprofessional Education & Practice,
19. https://doi.org/10.1016/j.xjep.2020.100327
Gehlert, S., & Browne, T. (2019). Handbook of health social work (3rd ed.). Wiley.
James, T. A. (2021, August 6). Teamwork as a core value in health
care. https://postgraduateeducation.hms.harvard.edu/trends-medicine/teamwork-corevalue-health-care
National Association of Social Workers. (2016). NASW standards for social work practice in
health care settings. https://www.socialworkers.org/LinkClick.aspx?fileticket=fFnsRHX4HE%3D&portalid=0
Zerden, L. D., Lombardi, B. M., & Richman, E. L. (2019). Social workers on the
interprofessional integrated team: Elements of team integration and barriers to
practice. Journal of Interprofessional Education & Practice,
17. https://doi.org/10.1016/j.xjep.2019.100286
Paper
MEDICAL SOCIAL WORK ON AN
INTERDISCIPLINARY HEALTH CARE TEAM
Interdisciplinary teams bring together diverse knowledge and skills and can result in
enhanced professional collaboration and cost-effective quality service—especially for
patients with complex health and social needs. Working effectively on a team is no small
feat. It requires skill and flexibility to coordinate and collaborate with multiple
professionals who possess different backgrounds and areas of expertise toward a
common goal: optimal patient care. As you think about teams this week, where do you see
yourself fitting in? How do you envision yourself navigating a team with many
personalities, roles, and scopes of practice found within it?
For this Assignment, you formulate a team to treat a specific patient. Keep in mind that
interdisciplinary teams are not one size fits all; their composition is highly dependent on
the setting and the patient’s needs.
To prepare:
•
•
•
Review the Learning Resources on interdisciplinary and integrated teams in
healthcare. Reflect on the composition of interdisciplinary teams, effective
collaboration among team members, and essential shared values.
Access the Case Studies interactive media in the Learning Resources and
navigate to Weeks 7 & 8. Read the cases of Claire and Bobby. Then,
choose one on which to focus your Assignment.
Think about how an interdisciplinary team would care for or provide service to
the identified patient.
BY DAY 7
Submit a 2- to 3-page paper in which you:
•
•
•
•
Summarize the patient population (age, gender, ethnicity or race, medical
condition or diagnosis) that the case study features.
Devise an interdisciplinary team for the patient in the case study, based on
patient needs and the healthcare setting. Define the team members’ roles.
Explain how you might collaborate with the members of the health care team.
Explain the essential values that are common to the members of an
interdisciplinary team with regard to patient care.
Use the Learning Resources to support your Assignment. Make sure to provide APA
citations and a reference list.
SW Practice Research II week 7 DQ, Peer response and 1 page paper
SELECTION OF A STATISTICAL ANALYSIS
APPROACH
Even at the data collection stage, the social work researcher needs to know what type of
data analysis will facilitate an answer to the research question. The researcher should
understand the purpose of each method of analysis, the characteristics that must be
present in the study for the design to be appropriate, and any weaknesses of the design
that might limit the usefulness of the results. Only then can the researcher select the
appropriate design.
Choosing the appropriate design enables the social work researcher to gather the most
relevant information about the relationship being studied. Notice that it is not the
statistical test itself that deems the research valid; rather, it is the research design. Social
workers must be aware of and adjust any limitations of their chosen design that may
impact the validity of the study.
In this Discussion, you examine a case study involving a quantitative design, determining
whether the statistical information supports the program’s efficacy and whether there are
limiting factors.
LEARNING RESOURCES
Required Readings
•
Dudley, J. R. (2020). Social work evaluation: Enhancing what we do (3rd ed.).
Oxford University Press.
o
•
•
Chapter 10, “Analyzing Evaluation Data” (pp. 255–275)
Flannelly, K. J., Flannelly, L. T., & Jankowski, K. R. B. (2018). Threats to the
internal validity of experimental and quasi-experimental research in
healthcareLinks to an external site.. Journal of Health Care Chaplaincy, 24(3),
107–130. https://doi.org/10.1080/08854726.2017.1421019
Document: A Short Course in Statistics Download A Short Course in
Statistics(PDF)
TO PREPARE
•
•
Review the Learning Resources on analyzing evaluation data, threats to
internal validity, and statistics.
Access the Social Work Case Studies media and navigate to the Chi-Square case
study.
•
As you review the case, consider the confounding variables—that is, factors
that might explain the difference between those in the program and those
waiting to enter the program.
BY DAY 3
•
•
•
•
Post a brief outline of the case study and consider the conclusion that “the
vocational rehabilitation intervention program may be effective at promoting
full-time employment.”
What statistical information shows whether the program was effective (or
not)?
Review the factors that limit the internal validity of a study (history,
maturation, testing, instrumentation, statistical regression, selection bias, and
attrition).
Select and explain which of these factors might limit the ability to draw
conclusions regarding cause-and-effect relationships.
BY DAY 6
Respond to at least two colleagues by explaining how that colleague might rule out one of
the confounding variables that they identified.
WORKING WITH DATA
Statistical analysis software such as SPSS is a valuable tool that helps researchers perform
complex calculations. However, to use such a tool effectively, the study must be well
designed. The social worker must understand the study’s purpose and select the most
appropriate design. The social worker must correctly represent the relationship being
examined and the variables involved. Finally, they must enter those variables correctly
into the software package.
In this Assignment, you analyze in detail the decisions made in the Chi-Square case study
and the relationship between study design and statistical analysis.
TO PREPARE
•
•
Access the Social Work Case Studies media and navigate to the Chi-Square case
study.
As you again review the case, this time focus on the purpose of the evaluation,
the choice of a chi-square statistic, and the research design. Consider what the
statistical results indicate about the program.
BY DAY 7
Submit a 1-page paper analyzing the relationship between study design and statistical
analysis used in the case study.
•
•
•
•
Explain why you think that the agency created a plan to evaluate the program.
Explain why the social work agency chose to use a chi-square statistic to
evaluate whether there is a difference between those who participated in the
program and those who did not. (Hint: Think about the level of measurement of
the variables.)
Describe the research design in terms of observations (O) and interventions (X)
for each group.
Interpret the chi-square output data and provide support for your
interpretation. (Hint: Review the value.) What do the data say about the
program?
Use the Learning Resources to support your paper. Make sure to include appropriate APA
citations and a reference list.
A Short Course in Statistics
This information was prepared to call your attention to some basic concepts underlying
statistical procedures and to illustrate what types of research questions can be
addressed by different statistical tests. You may not fully understand these tests without
further study. However, you are strongly encouraged to note distinctions related to the
type of measurement used in gathering data and the choice of statistical tests. Feel free
to post questions in the “Contact the Instructor” section of the course.
Statistical Symbols
µ mu (population mean)
α alpha (degree of error acceptable for incorrectly rejecting the null hypothesis,
probability that results are unlikely to occur by chance)
≠ (not equal)
≥ (greater than or equal to)
≤ less than or equal to)
ᴦ (sample correlation)
ρ rho (population correlation)
t r (t score)
z (standard score based on standard deviation)
χ2 Chi-square (statistical test for variables that are not interval or ratio scale [i.e.,
nominal or ordinal])
p (probability that results are due to chance)
Descriptives
Descriptives are statistical tests that summarize a data set.
They include calculations of measures of central tendency (mean, median, and mode)
and dispersion (e.g., standard deviation and range).
Note: The measures of central tendency depend on the measurement level of the
variable (nominal, ordinal, interval, or ratio). If you do not recall the definitions for these
levels of measurement, see https://www.questionpro.com/blog/nominal-ordinal-intervalratio/
You can only calculate a mean and standard deviation for interval or ratio scale
variables.
For nominal or ordinal variables, you can examine the frequency of responses. For
example, you can calculate the percentage of participants who are male and female; or
the percentage of survey respondents who are in favor, against, or undecided.
Often nominal data is recorded with numbers, e.g., male=1, female=2. Sometimes
people are tempted to calculate a mean using these coding numbers. But that would be
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meaningless. Many questionnaires (even course evaluations) use a Likert scale to
represent attitudes along a continuum (e.g., Strongly like … Strongly dislike). These too
are often assigned a number for data entry, e.g., 1–5. Suppose that most of the
responses were in the middle of a scale (3 on a scale of 1–5). A researcher could
observe that the mode is 3, but it would not be reasonable to say that the average
(mean) is 3 unless there were exact differences between 1 and 2, 2 and 3, etc. The
numbers on a scale such as this are ordered from low to high or high to low, but there is
no way to say that there is a quantifiably equal difference between each of the choices.
In other words, the responses are ordered but not necessarily equal. Strongly agree is
not five times as large as strongly disagree. (See the textbook for differences between
ordinal and interval scale measures.)
Inferential Statistics
Statistical tests for analysis of differences or relationships are inferential,
allowing a researcher to infer relationships between variables.
All statistical tests have what are called assumptions. These are essentially rules that
indicate that the analysis is appropriate for the type of data. Two key types of
assumptions relate to whether the samples are random and the measurement levels.
Other assumptions have to do with whether the variables are normally distributed. The
determination of statistical significance is based on the assumption of the normal
distribution. A full course in statistics would be needed to explain this fully. The key point
for our purposes is that some statistical procedures require a normal distribution and
others do not.
Understanding Statistical Significance
Regardless of what statistical test you use to test hypotheses, you will be looking to see
whether the results are statistically significant. The statistic p is the probability that the
results of a study would occur simply by chance. Essentially, a p that is less than or
equal to a predetermined (α) alpha level (commonly .05) means that we can reject a null
hypothesis. A null hypothesis always states that there is no difference or no relationship
between the groups or variables. When we reject the null hypothesis, we conclude (but
don’t prove) that there is a difference or a relationship. This is what we generally want to
know.
Parametric Tests
Parametric tests are tests that require variables to be measured at interval or ratio
scale and for the variables to be normally distributed.
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These tests compare the means between groups, which is why they require the data to
be at an interval or ratio scale. They make use of the standard deviation to determine
whether the results are likely to occur or very unlikely in a normal distribution. If they are
very unlikely to occur, then they are considered statistically significant. This means that
the results are unlikely to occur simply by chance.
The T-Test
Common uses:
• To compare mean from a sample group to a known mean from a population
• To compare the mean between two samples
o The research question for a t-test comparing the mean scores between
two samples is: Is there a difference in scores between group 1 and group
2? The hypotheses tested would be:
H0: µgroup1 = µgroup2
H1: µgroup1 ≠ µgroup2
•
To compare pre- and post-test scores for one sample
o The research question for a t-test comparing the mean scores for a
sample with pre and posttests is: Is there a difference in scores between
time 1 and time 2? The hypotheses tested would be :
H0: µpre = µpost
H1: µpre ≠ µpost
Example of the form for reporting results: The results of the test were not statistically
significant, t (57) = .282, p = .779, thus the null hypothesis is not rejected. There is not a
difference in between pre and post scores for participants in terms of a measure of
knowledge (for example).
An explanation: The t is a value calculated using means and standard deviations and a
relationship to a normal distribution. If you calculated the t using a formula, you would
compare the obtained t to a table of t values that is based on one less than the number
of participants (n-1). n-1 represents the degrees of freedom. The obtained t must be
greater than a critical value of t in order to be significant. For example, if statistical
analysis software calculated that p = .779, this result is much greater than .05, the usual
alpha-level which most researchers use to establish significance. In order for the t-test
to be significant, it would need to have a p ≤ .05.
ANOVA (Analysis of Variance)
Common uses: Similar to the t-test. However, it can be used when there are more than
two groups.
The hypotheses would be
H0: µgroup1 = µgroup2 = µgroup3 = µgroup4
H1: The means are not all equal (some may be equal)
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Correlation
Common use: to examine whether two variables are related, that is, they vary together.
The calculation of a correlation coefficient (r or rho) is based on means and standard
deviations. This requires that both (or all) variables are measured at an interval or ratio
level.
The coefficient can range from -1 to +1. An r of 1 is a perfect correlation. A + means that
as one variable increases, so does the other. A – means that as one variable increases,
the other decreases.
The research question for correlation is: “Is there a relationship between variable 1 and
one or more other variables?”
The hypotheses for a Pearson correlation:
H0: ρ = 0 (there is no correlation)
H1: ρ ≠ 0 (there is a real correlation)
Nonparametric Tests
Nonparametric tests are tests that do not require variables to be measured at
interval or ratio scale and do not require the variables to be normally distributed.
Chi-Square
Common uses: Chi-square tests of independence and measures of association and
agreement for nominal and ordinal data.
The research question for a chi-square test for independence is: Is there a relationship
between the independent variable and a dependent variable?
The hypotheses are:
H0 (The null hypothesis) There is no difference in the proportions in each category of
one variable between the groups (defined as categories of another variable).
Or:
The frequency distribution for variable 2 has the same proportions for both categories of
variable 1.
H1 (The alternative hypothesis) There is a difference in the proportions in each category
of one variable between the groups (defined as categories of another variable).
The calculations are based on comparing the observed frequency in each category to
what would be expected if the proportions were equal. (If the proportions between
observed and expected frequencies are equal, then there is no difference.)
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Other Nonparametric Tests
Spearman rho: A correlation test for rank ordered (ordinal scale) variables.
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