Printer Friendly

Unit Utilization of Internationally Educated Nurses and Collaboration in U.S. Hospitals.

With globalization increasing the mobility of nurses around the world, internationally educated nurses (IENs) are an important part of the nursing workforce in many countries. IENs are nurses who receive their primary nursing education outside of the country of current employment. In the United States, recruiting IENs has been a strategy to overcome the nursing workforce shortage for decades. While the actual number of IENs in the United States is difficult to capture, estimates indicate that about 5.6% to 16% of the over 3 million U.S. nurses or 168,000 to 480,000 nurses are IENs (Buerhaus et al., 2009; Spetz et al., 2014). Additionally, the number of IENs taking and passing the required U.S. nurse licensure exam (NCLEX-RN[R] exam) increased for 3 years in a row from 2014 to 2016 (Salsberg, 2016). IENs play an vital role in healthcare delivery and outcomes in the United States, including collaboration in patient care; yet, relevant empirical evidence is scarce.

While it is well acknowledged IENs help mitigate nursing workforce shortages, debates exist regarding the direct impact of IENs on quality of care and patient outcomes. To date, few studies have empirically examined this topic and the findings were inconsistent. Some researchers suggest IENs provided quality of care comparable to U.S.-educated nurses (Neff & Harman, 2013; Shen et al., 2015). In one study, researchers reported no difference in medication errors between IENs and U.S.-educated nurses (Shen et al., 2015). In another study, a higher proportion of IENs in hospitals was not associated with poorer quality of care or nurse and patient outcomes (Neff & Harman, 2013).

Meanwhile, other researchers observed lower satisfaction among patients who received care in hospitals with higher proportions of IENs (Germack et al., 2017; Mazurenko & Menachemi, 2016). Researchers also reported higher mortality rates of patients in hospitals that employed more IENs (Neff et al., 2013). Therefore, there remains a need to further examine the impact of IENs on health care. Specifically, the collaboration in care settings with different levels of IENs is an area necessitating special attention.

Improving collaboration, both intra- and interprofessional, in the interest of optimizing patient outcomes has become a priority for U.S. healthcare organizations and many others around the world (Brandt et al., 2014; Interprofessional Education Collaborative, 2011). Collaboration is recognized as an essential strategy for achieving the Institute for Healthcare Improvement's Quadruple Aim of better patient experiences, promoting provider well-being in the workplace, cost-efficient and effective care, and improved population health (Bodenheimer & Sinsky, 2014; Fink-Samnick, 2017; Havens et al., 2018; O'Connor, 2015; Sikka, Morath, & Leape, 2015). Efficient collaboration among and between nurses and other healthcare professionals is a fundamental aspect of quality work environments that often result in positive patient outcomes and satisfaction (Mazurenko & Menachemi, 2016).

While previous research has demonstrated variations in nurse-nurse collaboration and nurse-physician collaboration across acute care hospital units in the United States (Ma et al., 2018; Ma et al., 2015), it is unclear whether the presence of IENs influences nurses' intra and interprofessional collaboration with other care providers. The primary purpose of this study was to evaluate the association between levels of IENs and collaboration among nurses and between nurses and physicians in U.S. hospital units by utilizing data from the National Database of Nursing Quality Indicators (NDNQI[R]) (Press Ganey Associates, 2019). A secondary aim was to describe and compare nursing characteristics of units with different levels of IENs.


Research Design

This cross-sectional, observational study used data from NDNQI, a U.S. national data repository for comparisons of nursing care and nursing-sensitive outcomes at the patient care unit level. Two NDNQI datasets collected in 2013 were used to address the research questions, including the registered nurse (RN) survey and hospital administrative data. The institutional review board at the authors' institution approved the study.

Data and Sample

The NDNQI RN survey was used to create unit-level measures, including intra and interprofessional collaboration, utilization of IENs, and other unit characteristics (e.g., nurse staffing and education). The survey was designed to collect data from hospital units nationwide to assess, benchmark, and improve the nurse work environment in the United States. It is conducted each year electronically among hospitals that are NDNQI members. Eligible staff nurses complete the survey where data are collected about unit work environment and work content as well as demographics of nurse respondents. More information about the NDNQI RN survey can be found elsewhere (Ma et al., 2018). The eligibility criteria to participate in the survey are RNs (a) providing direct care to patients at least 50% of their time, (b) working on the current unit for 3 months or more, and (c) not employed as agency or contract nurses.

Collaboration and utilization of IENs were conceptualized as unit-level organizational characteristics, and thus, analyses were conducted at the unit level. A unit was included if it had a response rate of 50% or higher among all eligible nurses and at least five nurse respondents. These inclusion criteria were applied to ensure the aggregated unit-level measures had reasonable reliability. Researchers have recommended a response rate of 50% as a threshold for making accurate inferences when using aggregated data (Verran et al., 1995). Additionally, five types of adult units that were common and available in the majority of NDNQI hospitals were included: critical care, step-down, medical, surgical, and medical-surgical combined units.

NDNQI data analysts conducted data cleaning before delivering the datasets to the researchers. After applying the eligibility criteria, this study used reports of 24,034 nurses, of which 2,126 were IENs. The average number of respondents on a unit was 31 nurses. Final analyses were conducted among 958 units from 168 acute care hospitals in the United States.


Collaboration. Two scales were used to measure collaboration: the Nurse-Nurse (RN-RN) Interaction Scale and the Nurse-Physician (RN-MD) Interaction Scale. The RN-RN scale reflected the intraprofessional collaboration among nurses, and the RN-MD scale measured the interprofessional collaboration between nurses and physicians on a unit. The NDNQI adapted these two scales from the Index of Work Satisfaction (Stamps, 1997) and conducted pilot testing to assess the feasibility and reliability of the revised scales (Taunton et al., 2004). Each of the two scales had six items. Sample items from the RN-RN included "There is a good deal of teamwork among nursing staff," and "The nurses on our unit support each other." Sample items from the RN-MD scale included "In general, physicians cooperate with nursing staff," and "There is a lot of teamwork between nurses and doctors on our units." Nurses were asked to respond to each item using a Likert scale from strongly disagree (1) to strongly agree (6).

Given that collaboration was conceptualized as a unit-level organizational factor in this study, responses were aggregated from individual nurse respondents for each scale. More specifically, the mean of the six items comprising the respective scale for each nurse respondent was calculated; then, the mean of the scale scores across all the nurses on a unit were calculated to measure collaboration in that unit. A higher score indicated better collaboration. Preliminary analysis suggested the aggregated measures of collaboration at the unit level using NDNQI RN survey data were reliable (Ma & Stimpfel, 2018).

IENs. In the NDNQI RN survey, one question asked nurses to report where they received basic RN education, in the U.S. versus outside the United States. In this study, researchers calculated the proportion of nurses receiving primary RN education outside the United States among all nurse respondents on a unit to reflect unit level of IEN utilization. Based upon the percentage of nurses who were IENs, units were categorized into four groups: units with no IENs, some IENs but less than 10%, 10% or more IENs but less than 20%, and at least 20% IENs.

Covariates. Hospital and unit-level factors that might also influence collaboration were included as covariates in analyses. Hospital factors included ownership, bed size, teaching status, and Magnet[R] status. They were defined following the methods in research that also used NDNQI data (Ma et al., 2018).

Unit-level factors included unit type, nurse staffing, education, specialty certification, nursing tenure, and shift pattern. The patient-to-nurse ratio was calculated in each unit to reflect its staffing level. Unit education attainment was calculated as the proportion of nurses with at least a baccalaureate degree in nursing. Similarly, the proportion of nurses with a nursing specialty certificate from a national nursing association (e.g., CCRN, CEN, CNOR) was calculated to indicate the unit level of specialty certification. Nursing tenure was measured in two ways: the average years as an RN across nurses on a unit (RN tenure) and the average years working on the current unit (unit tenure). Suggested by previous research that unit shift pattern, particularly overtime, is associated with collaboration (Ma & Stimpfel, 2018), a variable indicating the proportion of RNs working overtime during last shift as a covariate was included.


Nurses who responded to the RN survey were first characterized overall and as those who were IENs. Descriptive analyses were used to present the characteristics of hospitals and units in this study. Analysis of variance was also performed to identify critical differences in collaboration and other unit characteristics by level of IENs. Finally, multi-level regressions with a hospital-level random intercept were employed to examine the relationship between unit IENs level and collaboration. The models were adjusted for hospital and unit characteristics. STATA version 14.0 (StataCorp LP) was used for all analyses. The statistical significance was set at p<0.05.


The characteristics of nurses, overall and IENs, are shown in Table 1. Of the 2,126 IENs, they were 44 years or older (SD=9) with an RN tenure of 19 years (SD=9) and unit tenure of 8 years (SD=7). The vast majority of IENs were female (88%), BSN-prepared (84%), and worked full-time (87%). Compared to the overall nurse sample, IENs were older and with longer RN tenure and unit tenure. They were more likely to have a baccalaureate degree and work full-time. Similar to the overall nurse sample, characteristics of IENs varied by unit type. Overall, critical care unit nurses were more likely to have a longer RN tenure and unit tenure, be male and White, and hold a baccalaureate degree and specialty certificate.

Distribution of nurses, overall and IENs, by hospital characteristics is presented in Table 2. The majority of IENs worked in nonprofit (76%), teaching (59%) hospitals. Slightly more than half of the IENs worked in large hospitals with at least 300 beds (54%) and Magnet hospitals (54%). Compared to the overall nurse sample, there were disproportionally more IENs in public (18% vs. 10%), non-teaching (41% vs. 35%), medium-size (43% vs. 37%), and non-Magnet (46% vs. 42%) hospitals.

At the unit level, nearly half (47%) of the 958 units did not have any IENs, and on average, 9% of nurses on a unit were IENs. Comparisons of collaboration and other unit characteristics by level of IENs are summarized in Table 3. The mean scores on the RN-RN interaction scale and the RN-MD interaction scale were 4.59 (SD=0.33) and 4.14 (SD=0.35), respectively. One-way variance analysis found units with higher levels of IENs had lower RN-RN collaboration, higher patient-to-nurse ratios, higher BSN rates, longer RN tenure, and less overtime.

Estimates of the association between unit utilization of IENs and collaboration among nurses and between nurses and physicians are presented in Table 4. The estimates indicate unit proportions of IENs were not associated with the levels of RN-RN collaboration after controlling for unit and hospital characteristics. Compared to units without IENs, units with 10%-20% IENs had lower RNMD collaboration, but this was not seen in units with IENs levels of <10% nor >20%. The models also suggest unit nurse staffing, overtime, unit tenure, and unit type were significantly associated with either RN-RN collaboration, RN-MD collaboration, or both. In addition, the estimates show units in nonprofit hospitals had higher RN-RN collaboration; units in large hospitals had lower RN-MD collaboration while units in Magnet hospitals had higher RN-MD collaboration.


This study is one of the very first studies that empirically examined the association between unit utilization of IENs and intra and interprofessional collaboration in U.S. hospitals. The findings provide unique insights into the complicated role IENs play in the U.S. healthcare system.

The use of IENs and their influence on quality of care and patient outcomes has been a controversial topic. IENs often face challenges when transitioning to practice in the United States because of the differences in culture, language, and healthcare system between home country and the United States (Ghazal et al., 2019; Moyce et al., 2016). Anecdotally there are concerns IENs may not perform up to the same level as peer U.S. nurses during patient care, including collaboration. In this study, having more IENs did not lead to a decrease in nurses' collaboration with other providers. Therefore, such concerns may not be necessary.

Another notable finding of this study is the results illustrating the influence of IENs' presence in shaping unit nursing characteristics. Units that had more IENs had a higher proportion of BSN nurses. This is, at least partially, because IENs are more likely to have a baccalaureate degree to qualify for the U.S. nursing licensure exam (NCLEX-RN). This finding is consistent with reports from the National Council of State Boards of Nursing (Budden et al., 2013). Previous research has also demonstrated hospitals with a higher proportion of BSN nurses are likely to have lower patient mortality and failure to rescue (Aiken et al., 2003). It is, therefore, possible that IENs contribute to improved health outcomes via promoting a better educated U.S. nursing workforce.

While higher turnover rates exist in hospital nurses (Park et al., 2016), this study indicated IENs stayed longer on their unit than peer U.S. nurses, and units with a higher proportion of IENs had a longer unit tenure among its nurses. Some IENs are recruited to the United States for the sole reason to work as an RN, while some come to work in the nursing profession after moving here with their family. Despite the different motivations for migrating to another country as an RN, previous qualitative research has shown IENs are more inclined to stay on a unit longer (Chun Tie et al., 2018). In other words, units with more IENs have a more stable nursing workforce or lower turnover rate. Lower turnover rates can contribute financially to a unit by reducing expenses on recruiting and hiring new nurses. It can also benefit collaboration among nurses over time.

IENs are often recruited to fill nursing position vacancies to mitigate the impact of the nursing workforce shortage on nursing quality and patient outcomes (Sherwood & Shaffer, 2014; Reinwald, 2015). Despite this, units in this study with higher proportions of IENs had higher patient-to-nurse ratios. IENs are likely to work in environments with heavier workloads that can result in worse patient outcomes (Ma et al., 2018; McHugh & Ma, 2013). Furthermore, previous research found patient-to-nurse ratios were negatively associated with collaboration on a unit (Ma & Stimpfel, 2018). This explains, at least partially, why units with higher proportions of IENs had worse collaboration in the binary analysis without adjusting nurse staffing. Another interesting finding of this study is that despite the higher patient-to-nurse ratios, units with a higher proportion of IENs had fewer overtime shifts. One possibility is those units might have longer shifts in general and thus were less likely to have nurses work overtime. Future studies are warranted to examine this interesting phenomenon.


Despite the use of a national database with a large sample, this study has limitations. It used an observational, cross-sectional design; therefore, findings are correlational, not causal. Although IENs' age, gender, race, education, work experience, etc., were reported, researchers were not able to specify the source country of IENs and whether English is a native or official language in nursing education. Also, though data from a unique nationwide database (NDNQI) were used, it should be noted hospitals' participation was voluntarily, and thus, some types of hospitals may be overrepresented or underrepresented. Researchers only examined collaboration of nurses with peer nurses and physicians. Further research should investigate the influence of IENs on nurses' collaboration with other healthcare professionals.


Findings from this study have implications for nurse leaders. Given the ongoing nursing workforce shortage, especially in rural areas, this study suggests nurse managers and administrators should not be reluctant to hire qualified IENs to fill position vacancies. It is also essential for nurse managers and U.S.-educated nurses to recognize IENs' contribution to a higher educated, more stable nursing team on their units. Recognition of an individual nurse's value can lead to a healthy work environment and workforce, which further contributes to high quality of care and improved patient outcomes. Hospitals that serve a highly diverse patient population may also benefit from IENs' additional language skills and knowledge of other cultures.

To help IENs better assimilate into the work environment and care team, hospitals may consider providing educational modules focusing on basics of the healthcare system within the employer hospital and in the United States, in addition to the standard work orientation program/training. Communication and teamwork workshops in the context of the U.S. healthcare system and culture would be another way to assist in integrating and orienting IENs. Such programs can be conducted in collaboration with IEN recruitment agencies. A peer mentoring program that pairs new IENs with a team of mentors, including a U.S.-educated nurse and a senior IEN, can also be explored.


The study is among the first to investigate the role of IENs in intra and interprofessional collaboration among nurses and physicians in patient care units in U.S. hospitals. The findings suggest higher proportions of IENs were not associated with lower levels of collaboration in U.S. hospital units. Additionally, this study indicates the presence of IENs can contribute to a more educated and stable nursing workforce in patient care units. $

Chenjuan Ma, PhD

Assistant Professor

New York University Rory Meyers College of Nursing

New York, NY

Lauren Ghazal, MS, FNP-BC, PhD student

New York University Rory Meyers College of Nursing

New York, NY

Sophia Chou, BSN, RN

Registered Nurse

New York University Langone Health

New York, NY

At the time this project was conducted, she was a Student, New York University Rory Meyers College of Nursing New York, NY

Emerson Ea, PhD, DNP, APRN, FAAN

Associate Professor, Assistant Dean of Clinical & Adjunct Affairs

New York University Rory Meyers College of Nursing

New York, NY

Allison Squires, PhD, RN, FAAN

Associate Professor

New York University Rory Meyers College of


New York, NY

Note: This research was supported through funding from The Sigma Theta Tau International Honor Society of Nursing (Fund #10361).

Acknowledgment: The authors thank Dr. Emily Cramer for her assistance in obtaining the data; Press Ganey Associates, Inc. for access to the data.


Aiken, L.H., Clarke, S.P., Cheung, R.B., Sloane, D.M., & Silber, J.H. (2003). Educational levels of hospital nurses and surgical patient mortality. JAMA, 290(12), 1617-1623.

Bodenheimer, T., & Sinsky, C. (2014). From Triple to Quadruple Aim: Care of the patient requires care of the provider. Annals of Family Medicine, 12(6), 573-576.

Brandt, B., Lutfiyya, M.N., King, J.A., & Chioreso, C. (2014). A scoping review of interprofessional collaborative practice and education using the lens of the Triple Aim. Journal of Interprofessional Care, 28(5), 393-399.

Budden, J.S., Zhong, E.H., Moulton, P., & Cimiotti, J. (2013). Highlights of the National Workforce Survey of Registered Nurses. Journal of Nursing Regulation, 4(2), 10.

Buerhaus, P.I., Auerbach, D.I., & Staiger, D.O. (2009). The recent surge in nurse employment: Causes and implications. Health Affairs, 28(4), w657-w668.

Chun Tie, Y., Birks, M., & Mills, J. (2018). The experiences of internationally qualified registered nurses working in the Australian healthcare system: An integrative literature review. Journal of Transcultural Nursing, 29(3), 274-284.

Fink-Samnick, E. (2017). Professional resilience paradigm meets the Quadruple Aim: Professional mandate, ethical imperative. Professional Case Management, 22(5), 248-253.

Germack, H.D., McHugh, M.D., Sloane, D.M., & Aiken, L.H. (2017). U.S. hospital employment of foreign-educated nurses and patient experience: A cross-sectional study. Journal of Nursing Regulation, 8(3), 26-35.

Ghazal, L.V., Ma, C., Djukic, M., & Squires, A. (2019). Transition-to-U.S. practice experiences of internationally educated nurses: An integrative review. Western Journal of Nursing Research. [E-pub ahead of print].

Havens, D. S., Gittell, J. H., & Vasey, J. (2018). Impact of relational coordination on nurse job satisfaction, Work engagement and burnout: Achieving the Quadruple Aim. Journal of Nursing Administration, 48(3), 132-140.

Interprofessional Education Collaborative. (2011). Core competencies for interprofessional collaborative practice: Report of an expert panel.

Ma, C., Park, S. H., & Shang, J. (2018). Inter- and intra-disciplinary collaboration and patient safety outcomes in U.S. acute care hospital units: a cross-sectional study. International Journal of Nursing Studies, 85, 1-6.

Ma, C., Shang, J., & Bott, M.J. (2015). Linking unit collaboration and nursing leadership to nurse outcomes and quality of care. Journal of Nursing Administration, 45(9), 435-442.

Ma, C., & Stimpfel, A. W. (2018). The association between nurse shift patterns and nurse-nurse and nurse-physician collaboration in acute care hospital units. Journal of Nursing Administration, 48(6), 335-341.

Mazurenko, O., & Menachemi, N. (2016). Use of foreign-educated nurses and patient satisfaction in U.S. hospitals. Health Cans Management Review, 47(4), 306-315.

McHugh, M.D., & Ma, C. (2013). Hospital nursing and 30-day readmissions among medicare patients with heart failure, acute myocardial infarction, and pneumonia. Medical Care, 57(1), 52-59.

Moyce, S., Lash, R., & de Leon Siantz, M. Lou. (2016). Migration experiences of foreign educated nurses. Journal of Transcultural Nursing, 27(2), 181-188.

Neff, D.F., Cimiotti, J., Sloane, D.M., & Aiken, L.H. (2013). Utilization of non-US educated nurses in US hospitals: implications for hospital mortality. International Journal for Quality in Health Care, 25(4), 366-372.

Neff, D.F., & Harman, J. (2013). Foreign-educated burses: Effects on nurse, quality of care, and patient-safety-indicator outcomes. Journal of Nursing Regulation, 4(1), 19-24.

O'Connor, B. (2015). Achieving the Quadruple Aim. The Triple Aim is great, but there's something it leaves out. Health Management Technology, 36(6), 24.

Park, S.H., Weaver, L., Mejia-Johnson, L., Vukas, R., & Zimmerman, J. (2016). An integrative literature review of patient turnover in inpatient hospital settings. Western Journal of Nursing Research, 38(5), 629-655.

Press Ganey Associates. (2019). National Database of Nursing Quality Indicators (NDNQI[R]).

Reinwald, C. (2015). Nursing shortage forces Genesis to hire from outside the U.S.

Salsberg, E. (2016). Changes in the pipeline of new NPs and RNs: Implications for health care delivery and educational capacity.

Shen, J.J., Neishi, S., VanBeuge, S., Covelli, M., Adamek, S., Gallegos, J., & Gardner, M.R. (2015). Comparing medication error incidents among foreign-educated nurses and U.S.-educated nurses. Journal of Nursing Regulation, 5(4), 4-10.

Sherwood, G., & Shaffer, F. A. (2014). The role of internationally educated nurses in a quality, safe workforce. Nursing Outlook, 62(1), 46-52.

Sikka, R., Morath, J M., & Leape, L. (2015). The Quadruple Aim: Care, health, cost and meaning in work. BMJ Quality and Safety, 24(10), 608-610.

Spetz, J., Gates, M., & Jones, C.B. (2014). Internationally educated nurses in the United States: Their origins and roles. Nursing Outlook, 62(1), 815.

Stamps, P. (1997). Nurses and work satisfaction: An index for measurement. Health Administration Press.

Taunton, R.L., Bott, M.J., Koehn, M.L., Miller, P., Rindner, E., Pace, K., ... Dunton, N. (2004). The NDNQI-adapted index of work satisfaction. Journal of Nursing Measurement, 72(2), 101-122.

Verran, J.A., Gerber, R.M., & Milton, D.A. (1995). Data aggregation: Criteria for psychometric evaluation. Research in Nursing & Health, 78(1), 7780.
Table 1.

Characteristics of Nurse Respondents by Unit Type

                      N of RNs           Age         Years as RN
                                      Mean (SD)        Mean (SD)
All RNs
Overall             24,034 (100)    37.59 (11.29)    10.04 (9.74)
Critical care      6,512 (27.09)    38.03 (10.95)    11.56 (9.93)
Step-down          3,775 (15.71)    36.15 (10.81)     8.56 (8.82)
Medical            4,712 (19.61)    37.58 (11.42)     9.30 (9.47)
Surgical           3,404 (14.16)    37.59 (11.84)   10.02 (10.37)
Medical-surgical   5,631 (23.43)    38.06 (11.48)     9.91 (9.69)

IENs Only
Overall              2,126 (100)     44.26 (9.15)    19.25 (9.17)
Critical care        465 (21.87)     45.87 (9.17)    22.16 (9.12)
Step-down            335 (15.76)     43.41 (9.03)    18.56 (8.63)
Medical              510 (23.99)     43.80 (8.82)    18.00 (9.08)
Surgical             249 (11.71)     44.82 (9.40)    19.83 (9.52)
Medical-surgical     567 (26.67)     43.64 (9.25)    18.22 (8.92)

                    Years on Unit   Female    White
                     Mean (SD)       (%)       (%)

All RNs
Overall             5.67 (6.32)     89.14     68.53
Critical care       6.45 (6.91)     84.03     73.83
Step-down           4.78 (5.25)     87.98     66.59
Medical             5.22 (5.90)     91.33     62.72
Surgical            6.06 (6.82)     92.09     70.70
Medical-surgical    5.51 (6.16)     92.16     67.26

IENs Only
Overall             7.63 (6.75)     88.44     6.59
Critical care       9.77 (7.74)     87.20     11.35
Step-down           6.90 (6.12)     86.24     3.02
Medical             6.73 (6.01)     89.60     3.79
Surgical            7.95 (6.94)     89.02     8.57
Medical-surgical    7.00 (6.43)     89.43     6.44

                     BSN    Certification    Full-Time
                     (%)         (%)            (%)

All RNs
Overall             67.98      64.27          23.87
Critical care       74.90      96.25          85.24
Step-down           70.41      84.82          87.49
Medical             66.57      44.64          83.56
Surgical            64.94        31.07            81.68
Medical-surgical    61.37        49.54            81.45

IENs Only
Overall             83.73        68.26            87.22
Critical care       85.59        96.75            86.15
Step-down           87.13        87.92            92.51
Medical             85.07        54.71            85.54
Surgical            82.59        37.55            87.10
Medical-surgical    79.51        58.47            86.50

Source: Press Ganey Associates, 2019

BSN = bachelor of science in nursing, IENs = internationally educated
nurses, RN = registered nurse

Table 2.
Distribution of Nurses, Overall and IENs, by Hospital Characteristics

                   Hospitals          IENs           All Nurses
                   n=168           (n=2,126)          (n=24,034)
                    N (%)             N (%)              N (%)


Not for profit     143 (85.12)    1,613 (75.87)     20,535 (85.44)
For profit         14 (8.33)       139 (6.54)        1,113 (4.63)
Government         11 (6.55)       374 (17.59)       2,386 (9.93)
Bed Size
Small (<299)       38 (22.62)       57 (2.68)        1,133 (4.71)
Medium (100-299)   80 (47.62)      918 (43.18)      8,871 (36.91)
Large (>300)       50 (29.76)     1,151 (54.14)     14,030 (58.38)
Academic center    20 (11.90)      727 (34.20)      7,709 (32.08)
Teaching           60 (35.71)      544 (25.59)      7,918 (32.94)
Non-teaching       88 (52.38)      855 (40.92)      8,407 (34.98)
Non-Magnet         111 (66.07)     979 (46.05)      10,162 (42.28)
Magnet             57 (33.93)     1,147 (53.95)     13,872 (57.72)

Source: Press Ganey Associates, 2019
IENs = internationally educated nurses

Table 3.
Unit Collaboration and Other Characteristics by Unit Level of IENs

                                                       [greater than
                            Unit Level                 or equal to]
                            of IENs 0%    >0 & <10%    10% & 20%
                            (n=453)        (n=227)     (n=116)
                            Mean (SD)     Mean (SD)    Mean (SD)

RN-RN collaboration *      4.62 (0.33)   4.60 (0.31)   4.50 (0.38)
RN-MD collaboration        4.17 (0.37)   4.12 (0.33)   4.09 (0.37)
Nurse staffing (patient-   4.89 (1.68)   4.91 (1.71)   5.43 (1.77)
to-nurse ratio) *
BSN rate *                 0.60 (0.20)   0.66 (0.20)   0.66 (0.19)
Years on unit *            5.32 (2.77)   5.75 (2.62)   5.82 (2.53)
Years as RN *              9.41 (4.11)   9.69 (3.80)   10.96 (3.61)
Certificate rate           0.62 (0.36)   0.61 (0.37)   0.58 (0.36)
Overtime *                 0.35 (0.20)   0.34 (0.19)   0.32 (0.22)

                               [greater than
                             or equal to] >20%    Overall
                                   (n=162)        (n=958)
                                  Mean (SD)       Mean (SD)

RN-RN collaboration *          4.54 (0.34)       4.59 (0.33)
RN-MD collaboration            4.15 (0.34)       4.14 (0.35)
Nurse staffing (patient-       5.20 (1.61)       5.01 (1.70)
to-nurse ratio) *
BSN rate *                     0.76 (0.14)       0.65 (0.20)
Years on unit *                6.45 (3.01)       5.67 (2.77)
Years as RN *                 13.57 (3.96)       0.37 (4.23)
Certificate rate               0.62 (0.34)       0.62 (0.36)
Overtime *                     0.25 (0.20)       0.35 (0.36)

Source: Press Ganey Associates, 2019

*Statistically significant difference

BSN = bachelor of science in nursing; IENs = internationally
educated nurses, MD = physician, RN = registered nurse

Table 4.
Association between Unit Level of IENs and Collaboration

                                            RN-RN Collaboration

                                              Model 1
                                         [beta] [95% CI]      p Value

Unit IENs

[greater than or equal to] 0 & <10%     -0.02 [-0.07 - 0.03]    0.444
[greater than or equal to] 10% & <20%   -0.12 [-0.19 - -0.05]   0.001
[greater than or equal to] 20%          -0.07 [-0.13 - -0.01]   0.014

                                              Model 2
                                         [beta] [95% CI]       p Value

[greater than or equal to] 0 & <10%     -0.01 [-0.06 - 0.04]     0.649
[greater than or equal to] 10% & <20%   -0.06 [-0.14 - 0.02]     0.161
[greater than or equal to] 20%          -0.01 [-0.10 - 0.07]     0.722

                                           RN-MD Collaboration

                                               Model 1
                                           [beta] [95% CI]      p Value

[greater than or equal to] 0 & <10%     -0.04 [-0.10 - 0.01]    0.124
[greater than or equal to] 10% & <20%   -0.08 [-0.15 - -0.01]   0.030
[greater than or equal to] 20%          -0.02 [-0.08 - 0.04]    0.547

                                             Model 2
                                         [beta] [35% CI]       P Value

[greater than or equal to] 0 & <10%     -0.04 [-0.10 - 0.01]   0.140
[greater than or equal to] 10% & <20%   -0.10 [-0.18 - 0.01]   0.025
[greater than or equal to] 20%          -0.07 [-0.15 - 0.02]   0.131

Source: Press Ganey Associates, 2019

Note: Model 2 was adjusted for hospital and unit characteristics.
Hospital characteristics included ownership, bed size, teaching
status, and Magnet status. Unit characteristics included unit type,
proportion of baccalaureate nurses, unit RN tenure, specialty
certificate rate, and overtime. BSN = bachelor of science in
nursing; IENs = internationally educated nurses, MD = physician, RN
= registered nurse
COPYRIGHT 2020 Jannetti Publications, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2020 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ma, Chenjuan; Ghazal, Lauren; Chou, Sophia; Ea, Emerson; Squires, Allison
Publication:Nursing Economics
Date:Jan 1, 2020
Previous Article:Use of Psychological First Aid for Nurses.
Next Article:Redesign of a Pediatric Hospital's Professional Advancement Program.

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |