04 Mar Research Articles Comparison and Contrast What articles have similarities in each section below?
Assignment Content
- In class, we learned how to write the introduction and conclusion of the Literature Review section, which includes:
- Introduction (6 sentences on p. 10a)
- Review of Literature (using notes on p. 13a – ONE objective summary is graded already — apply feedback)
- Analysis of Literature (compare and contrast activity on p. 16a)
- For this submission: You will submit your full draft of the Literature Review in a WORD document.
- YOU MUST CHECK YOUR SIMILARITY prior to submission here.
- Grading:
- Title Page = 10 pts
- Literature Review: Intro, Review of Literature, and Analysis of Literature WITH in-text citations and correct level headings:
- Introduction = 15 pts
- Review of Literature = 40 pts
- Analysis of Literature = 20 pts
- References page with ALL three research articles listed = 15 pts
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Research Articles Comparison and Contrast
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Research Articles Comparison and Contrast
1. What articles have similarities in each section below?
a. Methodology
Javed & Brishti (2020) and Köchling & Wehner (2020) both used systematic literature reviews. Both used a significant number of samples as Javed & Brishti (2020) reviewed 22 articles while Köchling & Wehner (2020) reviewed 36 articles.
b. Findings
All studies featured uncertainties and bias in regard to the use of AI in recruitment and hiring. Javed & Brishti (2020) and Köchling & Wehner (2020) indicated how AI could lead to unfairness in practice since algorithms are not inherently free from biases or discrimination. Similarly, van den Broek et al. (2019) showed that there are concerns with what is deemed fair when using AI. The study results indicated that there was uniformity amongst the main stakeholders (managers, candidates, and AI team) in that there was no clear-cut definition of what fairness is in regard to using AI, unlike the consensus that was evident prior to the introduction of algorithms in hiring. Wright & Atkinson (2019) also found out that there are uncertainties in whether AI can avoid biases as the feedback from the interviews, surveys, and observations showed that individuals have views that are conflicting.
c. Recommendations
Both Javed & Brishti (2020) and Köchling & Wehner (2020) indicated that there are both positives and negatives to using AI, and more research is required to enhance the potential of AI in the hiring process. They showed that there are grounds for using AI, but more research and developments should enable the elimination of bias.
2. What articles have differences in each section below?
a. Methodology
In contrast to Javed & Brishti (2020) and Köchling & Wehner (2020), who used systematic reviews, Wright & Atkinson (2019) used three different methods, which were interviews, online surveys, and observation. On the other hand, Van den Broek et al., 2019) used an ethnographic design that also featured observation.
b. Findings
While Javed & Brishti (2020) and Köchling & Wehner (2020) showed both the benefits and limitations of using AI, van den Broek et al. (2019) and Wright & Atkinson (2019) mainly showed that AI has significant shortcomings for it to be applied in the hiring process. They showed that the questions and unreliability of algorithms were too significant. The contrast is that van den Broek et al. (2019) and Wright & Atkinson (2019) did not provide a significant basis for using AI in the current circumstances without further development, while Javed & Brishti (2020) and Köchling & Wehner (2020) were different as they explored the opportunities that algorithms offer to hire managers.
c. Recommendations
While both van den Broek et al. (2019) and Wright & Atkinson (2019) cautioned against having too much confidence in the use of AI in hiring, Javed & Brishti (2020) and Köchling & Wehner (2020) on the other hand, provided some leeway in regards to the positive effects of applying algorithms. They showed that organizations could benefit from applying algorithms, whereas van den Broek et al. (2019) and Wright & Atkinson (2019) basically showed that there is more development required in the use of AI in order to achieve feasible outcomes in recruitment and hiring.
References
Brishti, J. K., & Javed, A. (2020). The viability of ai-based recruitment process: A systematic literature review. https://www.diva-portal.org/smash/get/diva2:1442986/FULLTEXT01.pdf
Elmira van den Broek, E., Sergeeva, A., & Huysman, M. (2019). Hiring algorithms: An ethnography of fairness in practice. https://core.ac.uk/download/pdf/301385085.pdf
Köchling, A Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795-848. https://doi.org/10.1007/s40685-020-00134-w
Wright, J., & Atkinson, D. (2019). The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting. Carmichael Fisher, 1-39. https://www.cfsearch.com/wp-content/uploads/2019/10/James-Wright-The-impact-of-artificial-intelligence-within-the-recruitment-industry-Defining-a-new-way-of-recruiting.pdf
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NOTES FOR TWO MORE RESEARCH ARTICLES 5
Notes for Two More Research Articles
Your Name Goes Here
Department of Name of Your Major, King Graduate School
KG 604: Research & Critical Analysis
Professor Ramlochan
2/14/2022
Notes for Two More Research Articles
W(5)H(1): New Research Article #1: Hiring Algorithms: An Ethnography of Fairness in Practice
1. Who conducted the research?
The study was conducted by Elmira van den Broek, Anastasia Sergeeva and Marleen Huysman
2. Why was the study completed (purpose / what researchers hoped to learn) ?
The purpose of the study was to investigate the perceived fairness in use of algorithms in hiring.
3. When was data collected (not the publication year) ?
Data was collected between October 2018 and April 2019.
4. Where was data collected (physical location) ?
Data was collected at multinational company in Europe called MultiCo.
5. How was data collected (methodology)? Cut as paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
The researchers used an ethnographic study design in the research process.
“We conducted an ethnographic in-depth study at the HR department of a large multinational company in Europe, “MultiCo” (pseudonym), that recently implemented AI to enable a fair recruitment process .”
“ We have conducted 7 months (726 hours) of non-participant observation of the work around the AI application in graduate recruitment – including 110 meetings and 27 selection events – in the period between October 2018 and April 2019” (van den Broek et al., 2019).
6. What were the findings? Cut as paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
The findings revealed that all groups involved in the study (HR, candidates, managers and AI team) contested the idea of fairness in using the algorithms in the hiring process.
“Our analysis of the specific practices and interactions of multiple stakeholders in the workplace shows that enabling fairness with AI can take a very different shape from what it promised, when put into practice. In particular, before the use of the AI application, the meaning of fairness was considered unproblematic and shared between stakeholder groups. However, as the various groups started working with AI in practice, they experienced mismatches between those notions of fairness that were inscribed and those implicit understandings of fairness that were important for daily work” (van den Broek et al., 2019).
W(5)H(1): New Research Article #2: The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting.
1. Who conducted the research?
James Wright and Dr David Atkinson
2. Why was the study completed (purpose / what researchers hoped to learn) ?
The research examined the impact of AI in recruiting employees.
3. When was data collected (not the publication year) ?
The study was conducted in 2019.
4. Where was data collected (physical location) ?
Data was collected in the UK.
5. How was data collected (methodology)? Cut as paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
The research featured use of three methodologies which were interviews, online surveys and observation.
“To obtain findings considering the impact of AI in the recruitment process for both employers and candidates, three research methods were used. These are highlighted below.
Research Method: Interviews Online Survey Observation” (Wright & Atkinson, 2019).
6. What were the findings? Cut as paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
The findings showed that there are uncertainties in use of AI with the candidate group majorly opposed to it.
“These are predominately preconceived ideologies on automation rather than AI. There are significant knowledge gaps in the industry, with many recruiters not understanding the technologies available to them. Candidates have a very negative opinion of the recruitment process and their considerations of automation follow this trend. There is clear motivation to change recruitment processes to appease candidates however it is contested whether AI is the solution to these complaints” (Wright & Atkinson, 2019).
W(5)H(1): New Research Article #3: Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
1. Who conducted the research?
Manjul Gupta, Carlos M. Parra and Denis Dennehy
2. Why was the study completed (purpose / what researchers hoped to learn) ?
The purpose of the study was to explore the role of Hoftede’s cultural values in influencing the inquiry of use of AI due perceived bias.
3. When was data collected (not the publication year) ?
The study was conducted in 2021.
4. Where was data collected (physical location) ?
Data was collected in the US.
5. How was data collected (methodology)? Cut as paste the paragraph below that describes the methodology and HIGHLIGHT the indicator words that specifically show you the methodology :
Data was collected through use of surveys.
“We first conducted a pilot survey of 60 MTurk users to ensure the readability and clarity of the seven scenarios pertaining to racial and gender bias. Following this, the main study was administered, and 387 completed responses were collected using MTurk in the United States” (Gupta et al., 2021).
6. What were the findings? Cut as paste the paragraph below that describes the findings and HIGHLIGHT the sentences that specifically show you the summary of findings:
The findings showed that AI is likely to lead to biased recommendations.
“ Indeed, AI-based recommendations may discriminate against some members of society more than others, and this we contend ought to be one of the most worrisome aspects of ubiquitous computing and generalized automation. Even though scholars have also been concerned, albeit recently, with proposing governance mechanisms to prevent AI-related misuses and abuses (Floridi & Cowls, 2019; Zuiderveen Borgesius, 2020), there still are reasons for concern. One such concern that we examine in this study is the extent to which individuals, owing to their individual-level cultural values, would be likely to question AI-based recommendations when perceived as racially or gender biased” (Gupta et al., 2021).
References
Gupta, M., Parra, C. M., & Dennehy, D. (2021). Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter?. Information Systems Frontiers, 1-17. 10.1007/s10796-021-10156-2
van den Broek, E., Sergeeva, A., & Huysman, M. (2019). Hiring algorithms: An ethnography of fairness in practice. Association for Information Systems https://core.ac.uk/download/pdf/301385085.pdf
Wright, J., & Atkinson, D. (2019). The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting. Carmichael Fisher, 1-39. https://www.cfsearch.com/wp-content/uploads/2019/10/James-Wright-The-impact-of-artificial-intelligence-within-the-recruitment-industry-Defining-a-new-way-of-recruiting.pdf
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RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 1
Racial Disparities in Healthcare Among Pregnant Women in the United States
Tamifer Lewis
Department of Public Health, Monroe College, King Graduate School
KG604-144: Graduate Research and Critical Analysis
Dr. Manya Bouteneff
December 4, 2022
I attest that I have used the checklist on pages 71-78 of my manual
I attest that I passed this paper through the free version of Grammarly
I attest that I incorporated ALL feedback from all previous assignments to make this paper
SHINE
RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 2
Racial Disparities in Healthcare Among Pregnant Women in the United States
Introduction
African American, American Indian, and Alaska Native women are up to three times
more likely to die from adverse pregnancy related outcomes, a disparity that increases with age
(Centers for Disease Control and Prevention [CDC], 2019). Researchers found a program which
provided support to African American women through group trainings, entailing of stress
reduction techniques, life skills development, and the building of social support. This enabled
mitigation efforts to be focused and geared around the factors that influence and contributed to
adverse pregnancy outcomes among the minority women within the community, thus reducing
and preventing negative pregnancy outcomes in women of color. In the United States, maternal
mortality and adverse health outcomes persist within the minority community, yet as racial
disparities in healthcare among pregnant women is a current preventable public health concern, it
is vital to understand the determinants of health that influences negative pregnancy related
outcomes in minority women, similar to one of California’s intervention programs, the Black
Infant Health Program (Nichols & Cohen, 2019).
Literature Review
Introduction to Literature Review
Research suggests that racial disparities in healthcare among pregnant women persists in
the United States (Zhang et al., 2013). Due to this continuous occurrence, it is vital to examine
the factors that contribute to the adverse outcomes in maternal health. The literature review
contained only research articles about factors that impacted and influenced disparities in
pregnancy outcomes. Factors that were reviewed were socioeconomic status, public health
insurance, race/ethnicity, and poverty status. The literature review was conducted using EBSCO
RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 3
Host and ProQuest databases from the Monroe College Library. The search terms used to
compile pertinent articles were racial disparities maternal health, adverse pregnancy outcomes,
and maternal health outcomes.
Review of Literature
Adverse Pregnancy Outcome Factors
Darling et al. (2021) conducted a study between 2001 and 2018 to examine the efficiency
of qualified interventions in preterm birth, small for gestational age, low birth weight, neonatal
death, cesarean deliveries, maternal care satisfaction, and coast effectiveness programs. A
systematic review was used to collect data from the United States, France, Spain, and the
Netherlands. The studies consisted of mostly non- Caucasian women from low-income
population ranging from 12 to 46 years of age and being between 20 to 32 weeks' gestation.
Interventional programs were implemented into three categories: group prenatal care, augmented
prenatal care, or a combination of both group and augmented prenatal care (Darling et al., 2021).
The researchers found that certain interventions, such as prenatal care and augmented care, or a
combination of both, may decrease adverse outcomes in small-for-gestational-age and preterm
birth, and could aid in increasing maternal care satisfaction. Interventions that worked on
enhancing coordination of care were found to result in providing more effective cost savings.
The researchers also found disparities in the quality of access to care in the vulnerable
population. There was insufficient evidence of suitable quality to confirm that the interventions
were successful at enhancing clinical outcomes in prenatal care for at risk populations (Darling et
al., 2021).
Similar observations were made in a study conducted by Nichols and Cohen (2020),
between 2006 and 2018 to examine the methods used to improve the results of maternal
RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 4
mortality in California. The study was conducted using a scoping review to evaluate research on
women and maternal health in the United States. The researchers used information from the US
Maternal Fetal Medicine Network to measure the percentage of studies where pregnant women,
women, and children were the main focus. The researchers also reviewed documentation on
healthcare policies and practices from California’s public health department, healthcare
foundation, and Maternal Quality Care Collaborative. Nichols and Cohen (2020) found that
although the health of fetus and children could be adversely affected by the health of the mother,
the majority of maternal programs in the United States places emphasis on the child. The
researchers also found four areas of concern in women health experiences, both in pre and
postnatal care. The problem areas entailed inadequate investment in women's health, inefficient
quality of care and avoidable caesarean delivers, expanding disparities in minority women and
women living in rural areas, and contradictory collection and distribution of data (Nichols &
Cohen, 2020).
Approaches to Improving Pregnancy Outcomes
In contrast to the preceding studies, Zhang et al. (2013) conducted a study between 2005
and 2007 to calculate the excessive rate of unfavorable outcomes in pregnancy within racial and
ethnic groups. The study also aimed to measure the possibility of Medicaid savings that are
linked to paid maternal care claims resulting from the inequalities that contribute to unfavorable
maternal outcomes. A cross-sectional study using Medicaid Analytic eXtract (MAX) data was
used to gather pregnancy outcome information from inpatient hospitals from 14 states (Florida,
Alabama, Arkansas, North Carolina, Georgia, Louisiana, Kentucky, Mississippi, Maryland,
Missouri, Tennessee, South Carolina, Virginia, and Texas). The study consisted of a little over 2
million patients who were insured with Medicaid and had a delivery code of maternal delivery
RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 5
stay. Zhang et al. (2013) found that, with the exception of gestational diabetes, African American
women showed the worst outcomes out of all unfavorable pregnancy outcomes. These disparities
are postulated as being multi-factorial, having causes stemming from complicated experiences
with racism, poverty, and complex healthcare interactions. It was also found that women covered
under Medicaid health insurance were more likely to have consistency in care from prenatal care
through delivery compared to their counterparts. However, due to participation in Medicaid
programs being influenced by reimbursement rates, some providers may choose to stop
accepting Medicaid patients because of reimbursement delays and low payment rates, which
could contribute to negative birth outcomes (Zhang et al., 2013).
Analysis of Literature
In the United States, the persistence of maternal mortality continues to be a problem area
in public health. The contributing factors that impact pregnancy outcomes persist in burdening
the U.S., leading to poor healthcare quality, and increasing health disparities. The studies used in
this literature review each used a different form of research methodology to collect data,
including systematic and scoping reviews and cross-sectional studies. Similarly, Darling et al.
(2021), Nichols and Cohen (2020), and Zhang et al. (2013) have emphasized the correlation
between race/ethnicity and financial status playing a part in influencing quality of care, access of
care, and pregnancy outcomes in pregnant minority women. To mitigate the disparities in
maternal health Darling et al. (2021) and Zhang et al. (2013) suggested that interventions should
be inspected and geared towards determining and eradicating the racial and ethnic disparities that
affect pregnancy-related outcomes. Whereas Nichols and Cohen (2020) suggested focusing on
exploring the distinctive experiences of particular at-risk subgroups of women, such as women in
RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 6
prison, who are of childbearing age, and the pregnant women who are less likely to pursue
prenatal care, such as undocumented women.
Discussion
Introduction to Discussion
There is current evidence that racial disparities in healthcare among pregnant women
continues to be a problem in the United States. In an article published by The New York Times
(Rabin, 2019), there has been a persistence and growth in racial disparity throughout the years
despite calls to take action to improve medical care access for women of color. Similarly, in a
study conducted by Nichols and Cohen (2019) mounting disparities continue amid women health
outcomes in the United States, primarily among race and ethnicity and within residents living in
urban and rural areas (Nichols & Cohen, 2019). These disparities directly affect African
American, Alaska Native and Native American Women (Rabin, 2019). When compared to other
high-income countries, the United States has substandard records in maternal health outcomes,
and while the rate of maternal mortality dropped across the world, America's maternal health
outcomes have worsened (Rabin, 2019).
Evidence-Based Recommendation
To reduce the disparities among minority women policy changes have been made.
Federal law enacted the Preventing Maternal Death Act providing states with grants to explore,
examine and investigate pregnancy related deaths for up to one year after the birth of a child
(Rabin, 2019). Also, The American College of Obstetrics and Gynecologists created new
guidelines in treating cardiovascular disease in pregnant women (Rabin, 2019). In 2014 Alliance
for Innovation on Maternal Health (AIM) was developed by the American College of Obstetrics
and Gynecology to collaborate with partners of the states and hospitals to gather information on
RACIAL DISPARITIES IN HEALTH IN PREGNANT WOMEN 7
safety measures being taken to improve maternal health outcomes, allowing partners to assess
and track program progress (Nichols & Cohen, 2019). In the study conducted by Nichols and
Cohen (2019), two out of the various programs that California implemented were the Black
Infant Health Program (BIH) and increasing the states income eligibility for pregnant women to
200% of the federal poverty level. With the implementation of these programs, mortality rates
decreased from 22.1% to 8.3% in the best practices toolkit, a program developed for hemorrhage
and high blood pressure during pregnancy. Altogether, California's maternal mortality rate
decreased by above 50% between 2006 and 2018 (Nichols & Cohen, 2019). To prevent negative
pregnancy outcomes in women of color, California used federal funds to develop programs that
focused on African American mothers and the health determinants that are influenced by social
and structural factors. The Black Infant Health Program provided support to African American
women through group trainings, entailing of stress reduction, life skills development, and
building social support (Nichols & Cohen, 2019). Nearly half of the babies born in the United
States are insured under Medicai
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