Hello, can someone please help correct and find these answers that I couldnt identify? I will post the article after the questions.

1)APA citation-

Ruggiero, L., Riley, B. B., Hernandez, R., Quinn, L. T., Gerber, B. S., Castillo, A., Day, J., Ingram, D., Wang, Y., & Butler, P. (2014). Medical assistant coaching to support diabetes Self-Care among Low-Income Racial/Ethnic minority populations. Western Journal of Nursing Research, 36(9), 1052-1073. https://doi.org/10.1177/0193945914522862

2)Purpose of article

to develop, implement, and evaluate the effectiveness of a culturally tailored medical assistant self-care coaching (MAC) intervention in low-income racial/ethnic minority populations with type 2 diabetes.

3)Research Question or hypothesis

4)Sample-

A total of 266 are included in the final sample for data analyses on self-care.

5)Variables -

Control - Model 1 unadjusted

Experimental- Model 2 adjusted for age, gender, duration of diabetes, baseline insulin site, baseline BMI, baseline outcome measures, depressive,

6)Research and design treatment-

Randomized controlled trial (RCT) that included 2 intervention group (Treatment as Usual [TAU] vs. MAC) 3 time point (baseline, 6 months, 12 months) repeated-measures design with three fixed factors: (a) race/ethnicity (African American, Hispanic/Latino), (b) clinic site (Clinics 1-4), and (c) diabetes medication regimen (non-insulin, insulin).

7)Measurement-

8)Findings-

ARTICLE:

Approximately 25.8 million children and adults in the United States (8.3% of the population) have diabetes (Centers for Disease Control and Prevention [CDC], 2013). Direct and indirect U.S. expenditures attributed to diabetes in 2012 were estimated at US$245 billion, including US$176 and US$69 billion in direct and indirect (disability, work loss, premature mortality) costs, respectively (American Diabetes Association [ADA], 2013a). The burden of diabetes, including prevalence and risk of complications, is greater for economically disadvantaged individuals and minority groups, especially Latinos and African Americans in the United States (Smedley, Stith, & Nelson, 2003). After adjusting for population age differences, 7.1% of non-Hispanic Whites, 11.8% of Hispanics, and 12.6% of non-Hispanic Blacks have been diagnosed with diabetes (CDC, 2013). Diabetes Self-Management Diabetes self-management is an important component of overall diabetes care. Research on self-care patterns has shown that it is complex and challenging in general (Delameter, 2006; Ruggiero et al., 1997). In addition, a recent 5-year longitudinal study found that after controlling for covariates, Hispanic and African American participants spent fewer days engaging in self-care activities compared with White participants (Trief et al., 2013). Research, including a systematic review (Peek, Cargill, & Huang, 2007), has examined the effectiveness of patient-level self-management education interventions in improving diabetes outcomes among racial/ethnic minorities receiving care at primary care clinics and support through community-based health workers and other resources. In general, self-management interventions, especially culturally tailored approaches, showed promise in improving diabetes outcomes. Continued 1054 Western Journal of Nursing Research 36(9) research is needed to help understand diabetes self-care patterns in racial/ethnic minority groups to further support cultural tailoring. There is strong evidence regarding the value of diabetes education, selfmanagement training, and support delivered by a professional diabetes educator; that is, a health care professional (e.g., nurse, dietitian, pharmacist) who has specialized training in diabetes care (ADA, 2013b; Duncan et al., 2011; Haas et al., 2012). Some primary care clinics may have limited or no access to formal diabetes education, training, and support programs (Emerson, 2006). Therefore, in clinical settings with limited resources, alternative or supplemental models, such as peer (e.g., Lorig, Ritter, Villa, & Armas, 2009) or technology-based (e.g., Dick et al., 2011; Khan et al., 2011; Walker et al., 2011) approaches, are needed to help provide culturally tailored diabetes education, training, and support to all patients. The American Association of Diabetes Educators (AADE; 2011) recognized the value of the use of multi-level teams, including para-professionals (e.g., certified medical assistants) and community health workers (CHWs), as important potential resources for providing diabetes education, self-management training, and support. There is growing evidence supporting the impact of CHWs on diabetes prevention and care (Norris et al., 2006; Ruggiero, Castillo, Quinn, & Hochwert, 2012). In addition, inclusion of certified medical assistants in diabetes health care teams has shown promise (Langford, Sawyer, Gioimo, Brownson, & O'Toole, 2007; Ruggiero et al., 2010). Medical assistants are commonly available in primary care clinics and may serve as integral members of health care teams. They have the potential to expand educational efforts and outreach to underserved populations, especially in clinical practices with limited resources. Like CHWs, they may also help connect with patients through shared racial/ethnic backgrounds or communities. Purpose The overall purpose of this study was to develop, implement, and evaluate the effectiveness of a culturally tailored medical assistant self-care coaching (MAC) intervention in low-income racial/ethnic minority populations with type 2 diabetes. The MAC intervention is innovative in the following ways: (a) delivered by medical assistants integrated into the clinical environment and specially trained and supervised in diabetes self-care and behavioral self management coaching, (b) guided by theoretical frameworks applying wellknown behavioral and counseling strategies, and (c) tailored for both the African American and Hispanic/Latino groups. The MAC intervention was designed to extend our reach and efforts in lower-resourced primary care Ruggiero et al. 1055 practices to support racial/ethnic minority groups. This article describes the joint effect of the intervention and ethnicity (Hispanic/Latino, African American) over time on self-care and A1C. Method Design The overall study was a randomized controlled trial (RCT) that included a 2 intervention group (Treatment as Usual [TAU] vs. MAC) 3 time point (baseline, 6 months, 12 months) repeated-measures design with three fixed factors: (a) race/ethnicity (African American, Hispanic/Latino), (b) clinic site (Clinics 1-4), and (c) diabetes medication regimen (non-insulin, insulin). This created homogeneous blocks of patients where patients within each block were randomly assigned with equal probability to intervention condition (TAU, MAC), thereby creating a randomized complete block design. For the current analyses, each intervention group is divided by ethnicity forming four groups: MAC-African American, MAC-Hispanic/Latino, TAU-African American, and TAU-Hispanic/Latino. Setting, Sample, and Recruitment This study was conducted at four primary care clinics that are part of a large ambulatory care network of federally qualified health centers (FQHCs) that serve predominately uninsured and Medicaid patients in Chicago and its surrounding suburbs. Specifically, participants were enrolled in this study based on the following inclusion criteria: (a) diagnosis of type 2 diabetes for at least 6 months, (b) receiving medication therapy for diabetes, (c) Hispanic/Latino or African American, (d) age equal to or greater than 18 years, (e) fluent in English or Spanish, (f) recent A1C value 6.5%, and (g) able to provide informed consent. Participants were not eligible if they met any of the following criteria: (a) pregnant or planning a pregnancy during the study period (this would necessitate significant intervention for intensive therapy), (b) serious comorbid medical (e.g., cancer, recent cardiac-related event) or mental health conditions (e.g., schizophrenia, dementia) or serious complications of diabetes (e.g., dialysis, limb amputation) that might affect participation, (c) not available by phone, and (d) currently enrolled in another diabetes-related research study or had a household member enrolled in this study. Initial contact occurred at the clinic through referral by health care providers or other clinic staff, especially medical assistants, or through self-referral based on recruitment materials in clinics or "word of mouth." The overall 1056 Western Journal of Nursing Research 36(9) recruitment strategy in all four clinics included informational sessions for clinic staff and the display and availability of informational materials, such as brochures and posters, in the patient waiting areas. Patients who were identified as potentially eligible and expressed interest in the study met with the project research specialist (RS) to confirm eligibility. A full-time bachelorlevel RS was placed at each clinic to conduct the eligibility screenings and all study self-report assessments with patients. If a patient was determined to be eligible by the RS, a detailed description of the study, including randomization, study protocol, and incentives (i.e., US$20 cash for baseline assessment and US$25 cash follow-up assessments), was provided in lay language in either English or Spanish according to the patient's preference. If the patient agreed to participate, the RS obtained the informed consent. A total of 1,294 patients from four clinics were identified by staff as possibly eligible (see Figure 1). Of these, 888 were screened by RSs and 501 were found to be ineligible based on inclusion-exclusion criteria. Of the 387 eligible patients, 117 declined to participate and 270 were consented and randomized. Four were removed from the sample by researchers due to the presence of major comorbidities that were identified after baseline. A total of 266 are included in the final sample for data analyses on self-care. Of the total analyzed sample, 4 from each group actively withdrew from participation and 39 and 47 were lost to follow-up for the MAC and TAU groups, respectively. All recruitment and consent materials were reviewed and approved by the Institutional Review Board of the University of Illinois at Chicago. Staff Training All staff were trained by the principal investigator (PI) and/or project manager on the study protocol relevant to their role. All staff also participated in Human Subjects Protection, Health Insurance Portability and Accountability Act (HIPAA), and cultural sensitivity training. RSs were responsible for the consent process, all self-report assessments, maintaining project materials and supplies at the clinic, and tracking-related activities. They were trained by the project manager in the use of a Tablet PC for conducting the computerbased self-report assessments, appropriate assessment techniques and data collection methods, and completion of the study activities tracking protocol. Certified medical assistants (MAs) served as our medical assistant coaches. In addition to the standard medical assistant training received during their education and through the health system, they received greater than 40 hr of initial project training, ongoing booster trainings as needed, and ongoing supervision by members of the multidisciplinary project team (i.e., physician, nurse CDE, psychologist, pharmacist) and/or relevant multidisciplinary experts. Trainings included basics of diabetes self-management, behavioral counseling strategies guided by the transtheoretical model (Ruggiero, 2000), empowerment theory (Funnell, Nwankwo, Gillard, Anderson, & Tang, 2005), motivational interviewing (Miller & Rollnick, 2002), five A's of counseling (Goldstein, Whitlock, & DePue, 2004), and the structured coaching protocol. Training on diabetes self-management was based on the Diabetes Empowerment Education Program (DEEP; Castillo et al., 2010). A master trainer conducted the 20-hr training covering basic information on diabetes epidemiology, its risk factors, clinical characteristics, complications, medication, self-management, nutrition, physical activity, and psychosocial factors. Screened (In-Person Screening by RSs) N= 888 Excluded: Ineligible (N= 501) Declined to participate (N= 117) Randomized and Consented (N = 270) Allocated to MAC = 136 Allocated to TAU =134 Analyzed = 134 Analyzed= 132 Lost to follow-up=39 Discontinued intervention= 4 Lost to follow-up=47 Discontinued participation= 4 Ineligible = 2 Ineligible= 2 Figure 1. Consort figure. 1058 Western Journal of Nursing Research 36(9) Intervention Conditions Primary care-enhanced TAU. Both groups received TAU at the participating clinics, which included regular visits with a primary health care provider, referrals for specialty care such as foot and eye exams, as well as visits to the endocrinologist when necessary, and basic education provided by a physician and/or other provider. Although there was one CDE available for referral at another site within the health center network, there were no CDEs on-site at any of the study clinics. To ensure that all patients in this trial had received basic diabetes education, all participants were given the "Diabetes: You're in Control" educational booklet at the baseline assessment contact. This booklet was developed by one of the sites and was tailored for low literacy and is available in Spanish. Additional activities related to the study for both groups included participation in repeated study assessments involving in-person con[1]tact, completion of the battery of behavioral and psychosocial measures, and a telephone prompt from the RSs to complete follow-up assessments. In addition, if the patient's score on the depression measure (described in Measures) indicated possible depression and/or suicidal ideation (i.e., above cutoff of 10; suicidal ideation item endorsed), a form summarizing the score was given to the patient's provider during the visit to facilitate any necessary follow-up. MAC intervention. The MAC intervention was designed to be delivered across a 12-month period with in-person contacts at regular clinic visits and follow[1]up phone calls between the clinic-based contacts. The MAC intervention was tailored using the following strategies: (a) MAs were of the same ethnicity as the predominant group served in a particular clinic (i.e., African American or Hispanic/Latino), (b) written educational materials were chosen to minimize literacy barriers (e.g., written at or below fifth-grade level; available in English and Spanish), (c) educational materials were culturally appropriate to either racial/ethnic subgroup, and (d) bilingual (Spanish/English) MAs were based in clinics serving Hispanic/Latino individuals. Self-care coaching was person-centered, individualized, and guided by theory (e.g., transtheoretical model, empowerment framework) and best practice counseling approaches (e.g., five A's of counseling, motivational interviewing). The focus was on helping the person learn the necessary information and skills to make informed self-care choices and changes. Content tailoring was based on the patient's choice from healthy eating, physical activity, blood glucose self-monitoring, medication adherence, foot care, smoking cessation, and healthy coping. MAC interactions were proto[1]col-driven and supported by a computer program that followed the five A's of counseling framework (i.e., assess, agree, advise, assist, and arrange). This Ruggiero et al. 1059 program provided specific questions to assess (e.g., self-care patterns, stage of change) for each self-care area addressed, agreed upon the agenda for the interaction based on patient preference, and offered suggestions for individualized stage-matched coaching feedback and educational resources to provide (i.e., advise, assist). The MA also supported the patient in arranging appointments and setting personal self-care goals. Written educational materials from available resources (e.g., National Diabetes Education Program's "4 Steps to Control Your Diabetes for Life") were provided and, where possible, were chosen to be culturally tailored, in the appropriate language, and matched to stage of change. Two very brief videos tailored to ethnic group and available in Spanish were available to describe proper foot care and meal portion size, where appropriate. The MAC intervention used two delivery approaches: face-to-face coaching during diabetes-related routine clinic visits and brief monthly telephone follow-up. Clinic-based (face-to-face) coaching sessions were designed to be brief (

<30 min) and be delivered quarterly during routine diabetes care visits. after the initial clinic-based coaching session, ma coaches initiated monthly follow-up calls 1-year intervention period, except for months when in-person clinic visits occurred. objectives of these were to (a) provide self-care coaching, (b) answer patient questions, (c) remind patients medical tests help arrange necessary appointments. telephone mac interactions designed brief (><15 min). if a patient missed or was not scheduled for quarterly clinic visit, telephone mac session attempted as substitute intervention contact. when needed requested guidance information regarding their medical care, the ma referred to primary care provider. considered member of health team, but dedicated research study and supervised by system physician on clinical matters multidisciplinary team (i.e., physician, nurse cde, psychologist) activities. managing threats internal validity number strategies were used minimize in this study, including (a) rs conducted all self-report assessments, (b) standardized questionnairesusing interactive computer-delivered approaches either english spanish, (c) once randomized condition, protocol tau group did include contact with assistant. after randomization, only other face-to-face interactions at 6-month 12-month visits conduct assessment. fidelity 1060 western journal nursing 36(9) delivered assistants also monitored several ways, review tracking reports, notes, charts project coordinator pi; occasional direct observation trained assistant; periodic pi role-plays during ongoing training supervision sessions. deviations from discussed correct any drift ensure delivery intended. measures assessment procedure summary diabetes self-care activities questionnaire (sdsca). outcomes measured sdsca. sdsca is 13-item measure frequency completion regimen over past 7 days. instrument assesses subscales general diet, specific exercise physical activity, blood-glucose testing, foot medication adherence, smoking average score (range =0-7 days). reliability, validity, normative data seven different studies (n =1,988) indicated that has adequate test-retest evidence sensitivity change (toobert, glasgow, & hampson, 2000). inter-item correlations within scales high (m =.47), moderate .40), multiple validated diet supported .23; toobert et al., spanish-version previous psychometric testing available. demonstrated consistency cronbach's alpha level .73 current sample. consistent studies, items capturing use displayed ceiling effect greater than 6 days week; reason, adherence excluded outcome. examines following subscales: exercise, self-testing, care. a1c. long-term glycemic control examined a1c values collected each time point. available, laboratory recorded electronic paper records. an value available via record point, it obtained dca 2000+ analyzer bayer (mishawaka, indiana). covariates included baseline sociodemographic characteristics age gender), length duration (in years), insulin ruggiero al. 1061 (yesno), body mass index (bmi), depressive symptoms, self confidence. across considered. categorical variable participant point continued newly prescribed). measurements height weight calculate bmi. previously (phq-9; kroeke, spitzer, williams, 2001) evaluate symptomatology. nine-item phq-9 effective screening tool depression reliability reported community samples. uses 4-point likert-type scale (0 =not 3 =nearly every day) probe extent which individuals have been troubled symptoms last 2 weeks; possible scores range 0 27. found valid african american hispanic> .15). Study completion rates based on self-care data for the four intervention/ethnicity groups ranged from 59.0% (MAC-Hispanic) to 79.4% (TAU-African Americans). An average of four MAC contacts was completed per participant across the 1-year period, and the majority of the intervention contacts were clinic-based. Comparisons of Intervention/Ethnicity Groups Over Time Table 3 presents descriptive statistics (means and standard errors) on the SDSCA scales as a function of intervention condition, ethnicity, and time. Table 4 presents the F and p values for the main and interaction effects for Models 1 and 2. Across all of the SDSCA scales, there was a significant overall effect for time (both unadjusted and adjusted models; all ps < .01). The pattern of results was similar for the unadjusted and adjusted models (Models 1 and 2, respectively). In the unadjusted model, post hoc analyses revealed significant improvements in all SDSCA scales from baseline to 6 months (all ps < .05). There were also significant post hoc comparisons between baseline and 12 months on General Diet, Specific Diet, and Foot Care outcomes (all ps < .01). There was also a significant improvement in Foot Care from 6 to 12 months (p < .01). In the adjusted models, significant improvements were observed from baseline to 6 months on all SDSCA scales (ps < .01) and from baseline to 12 months (General Diet and Specific Diet: p < .01; Physical Activity and Glucose Testing: p < .05). Only with respect to Foot Care was there a significant change from 6 to 12 months (p < .01). Unadjusted group main effects were observed on A1C (p < .001), Specific Diet (p < .02), Glucose Testing (p < .01), and Foot Care (p < .04). Significant adjusted group differences were observed on the Glucose Testing and Foot Care measures. In the adjusted model, Hispanics in both conditions evidenced significantly higher levels of foot care compared with their African American counterparts. In contrast, MAC-African Americans had significantly higher levels of glucose testing than Hispanics in both conditions (ps < .01). Finally, whereas no significant Group Time interaction was observed in the unadjusted analyses, a significant interaction effect was found for Foot Care (p < .01) after adjusting for covariates. Post hoc analyses revealed a significant change in foot care behavior over time in every group except MAC-African American. Pairwise comparisons between all waves were statistically significant among MAC-Hispanics, with the greatest change occurring between baseline and 12 months (t = 5.80, p < .01) and between 6 and 12 months (t = 3.36, p < .01). The TAU-African American group only evidenced a significant change from 6 to 12 months (t = 2.46, p < .05). In contrast, TAU-Hispanics evidenced significant change from baseline to 12 months (t = 3.91, p < .01). Over the course of the study, MAC-Hispanic participants evidenced the greatest improvement in foot care behavior, followed by TAU-Hispanics. Discussion The overall findings of this study demonstrate that all groups reported improvements in self-care across time, especially from baseline to 6 months, but no intervention effect was found and no differences were found for A1C. In addition, differences were found in the patterns of self-care changes for the two ethnic groups. In particular, Hispanic/Latino participants in both conditions evidenced both significantly higher levels and greater rate of change in foot care compared with their African American counterparts. In particular, Hispanic/Latino participants in the MAC condition reported the greatest improvement in foot care behavior at the end of the intervention (i.e., reported checking their feet nearly daily on average). In addition, African Americans in the MAC condition reported significantly higher levels of glucose testing than Hispanics in both intervention conditions. The pre-intervention patterns across self-care behaviors are consistent with those found in other studies indicating the highest levels of reported adherence to the medication regimen and the greatest challenges in getting regular physical activity (Delameter, 2006; Ruggiero et al., 1997). In African Americans, the patterns found for self-care in this study are consistent with those found in a similar sample (Tang, Brown, Funnell, & Anderson, 2008). The pre-intervention levels of self-care from highest to lowest (excluding medication) for African American participants were foot care, glucose self-testing, healthy eating, and physical activity and those for Hispanic/Latino participants were foot care, healthy eating, glucose self-testing, and physical activity. Studies focused on examining innovative approaches to enhancing diabetes self-management in low-income racial/ethnic populations have had mixed results. Although some controlled studies found significant differences between intervention groups on diabetes self-care measures, other studies did not (e.g., Anderson, Christison-Lagay, Villagra, Liu, & Dziura, 2010; Frosch, Uy, Ochoa, & Mangione, 2011; Khan et al., 2011). In particular, one study (Anderson et al., 2010) with an intensive 1-year telephone-based intervention delivered by nurses in an ethnically diverse population and setting similar to that of the current study did not find differences in self-care or glycemic control. The results across these studies indicate the need for more controlled studies to help identify effective culturally tailored diabetes self-management interventions for low-income racial/ethnic minority populations. The overall goal was to examine the real-world effectiveness of a culturally tailored medical assistant coaching intervention designed to improve diabetes self-care in low-income minority adults with type 2 diabetes. The limitations, challenges, and lessons learned regarding study methodology, population, and setting will be described. Ruggiero et al. 1069 This study examined self-care outcomes using one self-report measure; therefore, measurement issues could have affected the results. Although efforts were made to maximize validity (e.g., measure validated in English and Spanish) and accuracy in the assessment process (e.g., interactive computer assessment with audio in English and Spanish), this study may still have suffered from common challenges related to self-report data, such as social desirability bias. Future studies should ideally use multiple reliable and valid measurement approaches. In particular, as a ceiling effect was experienced in the measurement of medication adherence, multiple methods (e.g., pill counts, electronic monitoring, pharmacy refill data) and new measures (Morisky, Ang, Krousel-Wood, & Ward, 2008) should be considered in assessing medication use/adherence as it may directly influence A1C. Although a variety of strategies were used to minimize threats to internal validity, challenges were encountered in delivering the research intervention protocol within the real-world clinic setting. For example, contamination across interventions was possible because blinding of MA coaches and clinicians was not feasible, and patient contact by MAs may have occurred in the course of their general clinic duties, but was considered consistent with "treatment as usual." Also, clinic staff-initiated co-interventions occurred (e.g., support group started in one clinic). Setting-related barriers to delivering the planned quarterly clinic contacts were experienced, including variability in spacing/scheduling of routine diabetes visits and attrition related to an increase in the clinic fee structure. Although the MAC intervention was designed to be intensive and the researchers monitored MAC procedures for fidelity, the majority of participants did not receive the intended dose of the intervention. Two potential reasons for the low dose included patients frequent "no showing" for scheduled routine clinic appointments and challenges in reaching participants by phone for follow-up contacts between clinic visits despite multiple call attempts. In general, participants had multiple real-world barriers (e.g., scheduling conflicts, transportation problems, frequent disconnected phones) to accessing this intensive intervention. Many lessons were learned from this study. The results underscore the importance of identifying and understanding differences in self-care pat[1]terns between racial/ethnic groups and unique barriers and facilitators to self-care. For example, the improvement found in our study for the Hispanic/ Latino group on foot care may be related to the culturally and language-tailored video on foot care shown by the MA coach. Future research is needed with large and diverse samples with respect to race/ethnicity and socioeconomic status to better understand diabetes self-care patterns and correlates 1070 Western Journal of Nursing Research 36(9) and help identify effective culturally tailored self-care interventions. Another study from our work (Hernandez et al., 2013) examined potential correlates of self-care at baseline and found that sociodemographic and psychosocial factors help explain the different patterns found across ethnocultural groups. It is notable that each of the four groups evidenced improvements in selfcare across time. One hypothesis that may help explain our results is the provision of a standard diabetes education booklet (i.e., enhanced TAU) and increased attention related to diabetes self-management may have been a useful intervention. Therefore, the model of extending educational opportunities in lower-resourced settings with trained and supervised medical assistants deserves further consideration. A large randomized controlled study, ideally randomizing at the clinic level and blinding clinicians to intervention, could further examine the effectiveness of a MAC model in other groups/settings. If effective, examination of cost-effectiveness would be beneficial and should consider the resources needed to provide the MAC intervention, such as initial training and ongoing supervision. In addition, innovative methods are needed to minimize attrition, such as maintaining contact through social networking. Innovative approaches are needed to overcome the barriers experienced, both at the population and setting levels. To overcome access barriers at the patient level, alternative approaches are needed to provide culturally tailored educational activities that are convenient (e.g., evenings, weekends, on[1]demand) and delivered where people work, play, pray, and spend the day. For example, technology-based interventions (Haas et al., 2012) and community[1]or peer-based approaches (Lorig et al., 2009; Norris et al., 2002) may help remove access barriers, extend the reach, and improve effectiveness of clinic[1]based interventions. To address other barriers experienced, an integrative comprehensive approach (e.g., Peek et al., 2012) that intervenes at multiple levels (patients, practice teams, communities, health systems) based on the chronic care model (Wagner et al., 2001) should be considered.