1. Wilson, Barbara L. PhD, RNC
  2. Blegen, Mary PhD, RN, FAAN


Numerous studies have identified a relationship between staffing levels and nurse-sensitive outcomes for medical and surgical patients, but little has been published on the impact of nurse-sensitive outcomes for the childbearing family and even less that examines the relationship of intrapartum staffing on adverse perinatal outcomes. Using a derivation of Donabedian's classic structure, process, and outcomes framework, a model is proposed, which would allow obstetrical primary care providers and administrators alike the opportunity to examine the influence of nurse staffing on adverse obstetrical events, including unanticipated cesarean birth in low-risk women or newborn intensive care unit admissions. It is recognized that hospitals carry a significant burden in the prevention of adverse outcomes that range from nurse staffing levels to the internal process and infrastructure of the hospital setting. Patient outcomes are a direct result not only of the patient's health status and characteristics (eg, socioeconomic position and ethnicity), but also of interactions with the healthcare delivery system. As such, the opportunity to examine hospital characteristics (structure and processes) that may be detrimental to safe patient outcomes is of paramount importance in providing optimal outcomes for childbearing women and their families.


Article Content

Several comprehensive studies have confirmed the relationship between staffing levels and nurse-sensitive outcomes for medical and surgical patients, including pressure ulcers, hospital-acquired pneumonia, patient falls, medication errors, and failure to rescue.1-6 Ten years after these seminal studies, little has been published on the impact of nurse-sensitive outcomes in obstetrical (OB) patients and even less that assesses the influence of nurse staffing on adverse OB events, such as unanticipated cesarean birth in low-risk women or the incidences of admissions to newborn intensive care units (NICU). Low staffing numbers in intensive care units (including NICUs) are associated with increased morbidity and mortality.7,8 In addition, numerous studies in the 1990s explored the link between neonatal morbidity and mortality to organizational variables, including the location and level of care 7,9,10 and volume-adjusted workload.11 Newborn intensive care units' organizational variables (including staffing) are influential in newborn morbidity and mortality rates, although there are few studies that measure labor and delivery (L&D) variables on neonatal outcomes. Previous studies have focused on staffing in the NICU once the infant has already been admitted and treatment has been initiated. It stands to reason that if organizational variables in the NICU (such as staffing) affect newborn outcomes, staffing in L&D may also exert a significant influence on neonatal well-being.


Measuring the influence of L&D staffing on patient safety and adverse outcomes is difficult for numerous reasons. Many hospitals (depending on hospital size and patient acuity) use cross coverage, which allows nursing personnel to work between and among L&D, OB triage, antepartum, and postpartum areas, and therefore, measure "maternal child staffing" as a single unit. In addition, L&D personnel often provide staffing for patients undergoing varying surgical procedures, such as cesarean births, dilatation and curettage for retained placenta or incomplete abortions, and tubal ligations following delivery. Because most productivity measures examine nursing hours per patient day and use the midnight census as the point in time that constitutes a "patient day," these surgical patients are lost in the analysis, given that they rarely remain in L&D overnight. Hence, the midnight census is an ineffective measurement for productivity in L&D units. Because of the inherent challenges in defining, isolating, and measuring the influence of L&D staffing on patient safety and nurse-sensitive outcomes, little has been done to address appropriate staffing levels or optimal skill mix for this patient population from a data-driven outcomes perspective.


Therefore, the purpose of this article is to propose a model for the measurement and evaluation of L&D staffing on patient outcomes. Although there are challenges in measuring the contextual factors and influences of the L&D nurse on maternal and newborn birth outcomes, this knowledge could provide vital information for healthcare providers, hospital administrators, and healthcare delivery systems in designing and implementing optimal staffing models for safe, cost-effective intrapartum care.



It is believed that the proposed model could be used to test whether staffing patterns in L&D impact the type of delivery (vaginal or cesarean) or contribute to selected adverse maternal and newborn outcomes (Figure 1). The model is based on Donabedian's classic structure, process, and outcomes (SPO) model, which is useful in evaluating healthcare quality outcomes.12 Although Donabedian's model has been used for numerous studies on health services and the assessment of patient outcomes,13-18 the key to its successful application is in the manner in which the constructs under consideration are operationalized.

Figure 1 - Click to enlarge in new windowFigure 1. Conceptual model for measuring nurse-sensitive perinatal outcomes. Derived from Donabedian.

In the proposed model, it is suggested that structure, process, and outcome be measured by various proxy variables that reflect organizational structure (hospital ownership, teaching status, and bed size), processes of patient care (nurse staffing and skill mix), and healthcare outcomes for low-risk, first-time (primiparous) women with a term singleton fetus. It is important to begin by identifying the link between nursing care and nurse-sensitive outcomes for the OB woman. This will include a discussion on patient safety indicators (PSIs) and inpatient quality indicators (IQIs) developed by the Agency for Healthcare Research and Quality (AHRQ) to screen for problems in the healthcare delivery system, followed by a review of the literature on what is known about nurse staffing levels and adverse events. This will be followed by a detailed description of the proposed model and identification specific empirical measurements of each construct, including how these measurements have been validated in previous studies as indicators of patient safety.



One challenge in establishing the link between nursing care variables and unfavorable perinatal outcomes is determining which OB variables are sensitive to nursing intervention. Nurse-sensitive outcomes are one of the markers reflecting institutional quality of care and are defined as a condition, state, or perception (of either the patient or family caregiver) that is responsive to the actions of the nurse.1 In one study, Gagnon et al19 evaluated the effect of continuity of nursing care (the number of nurses and number of switches in registered nurse [RN] care providers per laboring woman) and the subsequent link to cesarean births, finding an independent association of the number of nurses caring for a woman in labor with cesarean section. Likewise, Radin et al20 found that nurses' care during labor was a significant determinant in influencing cesarean birth rates in low-risk, healthy primiparous women. These results differed from those in Hodnett et al's21,22 randomized control of childbearing women, where no significant differences in cesarean section rates were found when comparing women who received continuous labor support by a specially trained nurse with women who received usual care. Although the results of these studies are inconsistent in establishing a relationship between labor support and cesarean births, they highlight the importance and feasibility of examining possible associations of nursing care and birth outcomes for the intrapartum woman.


A second challenge in establishing a link between nursing care variables and an unfavorable or adverse outcome is determining which OB outcomes should be considered "adverse." Although the American College of Obstetrics and Gynecology has been reluctant to define unanticipated cesarean birth as an adverse event in low-risk women, noting an "absence of significant data on the risks and benefits of cesarean delivery,"23(pA27) others have established clear and compelling risks associated with cesarean birth, including postpartum cardiac events; major puerperal infection; anesthetic complications; thromboembolism; hemorrhage24; increased risk for respiratory distress in the neonate25; greater complications in subsequent pregnancies, including uterine rupture and anomalies with placental implantation;25 and future difficulties in reproductive life.26 Moreover, although the absolute difference between the risk of maternal morbidity associated with cesarean births and the risk of maternal morbidity associated with vaginal births is small, the former is higher and must be considered when deciding on appropriate delivery strategies for the childbearing family.


To help define and measure adverse hospital events, AHRQ developed PSI to screen for problems that patients experience as a result of being in the healthcare delivery system, making them amenable to changes in the hospitals' processes or procedures. Examples of PSIs in the perinatal arena include instrument-assisted births (PSI 18) and newborn injury (PSI 17). Likewise, AHRQ's IQI further reflect hospital care, including the utilization of procedures that have the potential for overuse, underuse, or misuse such as cesarean births (IQI 33) with lower rates representing better quality.27,28 Cesarean section rates for low-risk, first-birth mothers are one of the new Joint Commission Expert Perinatal Measures, effective April 2010,29 which track the proportion of live cesarean births at or beyond 37-week gestation to primiparous women with a singleton, vertex (head first) presentation. In addition, cesarean sections are 1 of the 17 indicators from the National Quality Forum's Perinatal Care Performance Measures,26 with a focus on the proportion of cesarean births following elective induction and early-labor admissions.



Nursing staffing levels must be viewed in the same light as other patient safety measures.30 Of the 1609 sentinel events reported to the Joint Commission as of March 2002, nurse staffing was a factor in 24% of those events.31 Sentinel events (unanticipated occurrences in the healthcare setting that result in serious injury or harm and are not related to the expected course of the patient's hospitalization) occur in nearly all hospital settings, contributing to significant increases in cost and additional lengths of stay. Healthcare institutions are now held accountable for the appropriateness of care provided and skill of execution for that care. The proliferation of studies evaluating patient outcomes attests to the emphasis on quality of care delivery, where the significance of each variable and any interactive influence must be considered when looking at patient care outcomes. Although measuring and monitoring quality with limited hospital resources present a challenge, "it is impossible to manage for quality without measuring and improving quality." 32(p108) In today's healthcare environment, nurses and nurse leaders are expected not only to monitor and measure patient care outcome, but also to evaluate the processes used to achieve those outcomes. Nurse staffing is among the most fundamental activities used in the provision of care.


It is not clear whether the well-published association between increased nurse staffing and better patient outcomes in medical/surgical patients reflects a cause-and-effect relationship, although studies consistently suggest that hospital mortality rates are 6% to 16% lower for each additional RN full-time equivalent patient day in medical and surgical patients, with additional significant reductions noted in "failure to rescue" rates and hospital-acquired pneumonia.33-36 Hospitals with better nurse staffing have better clinical outcomes,37 and higher staffing levels are associated with a 2% to 25% reduction in adverse events.38 In addition, studies on nursing skill mix suggest that better outcomes are associated with higher proportions of RNs39-41 as opposed to using more licensed practical nurses (LPNs) or unlicensed assistive personnel (UAPs). These studies have not included OB women who may have different care needs (eg, they may require more basic-comfort measures, which can be accomplished by UAPs or LPNs) and therefore, may be amenable to a lower RN/LPN or RN/UAP skill mix, with outcomes comparable to higher skill-mix combinations. This is one area that has not been adequately explored.



The proposed model could test whether a relationship exists between L&D staffing and type of delivery (cesarean or vaginal) or adverse maternal event (Figure 1). The model is based on Donabedian's classic SPO model, often used to evaluate health outcomes.



Donabedian defined structure as the organizational variables used in the provision of care, including teaching status, research functions, and general organizational characteristics. In the past, organizational structure was known to be a significant variable in the likelihood of cesarean births before 2000, because teaching hospitals had lower cesarean rates, even with higher-risk OB patients.42 The likelihood of a cesarean birth was significantly lower in teaching-designated hospitals for all birth complications, with earlier studies noting the most pronounced protective effect of teaching status occurring in newborns complicated by fetal intolerance, regardless of payment source, maternal age, or OB risk factors.43 Given the prolific increase in cesarean births in all healthcare facilities over the past decade, it is unclear whether similar findings would occur in today's climate and is one of the factors that should be studied. Structure ideally should include system characteristics such as hospital ownership (for profit, not-for-profit, or federal ownership such as the Indian health services), teaching status, and number of licensed beds (which may serve as a proxy for availability of technological resources). The availability of subspecialty care is also generally considered a proxy for level of care, resulting in better patient outcomes.


In one study that examined the influence of institutional characteristics on birth outcomes in more than 62000 ethnically diverse childbearing women, for-profit hospitals had significantly lower instances of women with prolonged labor or NICU admissions than not-for-profit facilities after controlling for other confounding variables, such as maternal age, parity, ethnicity, insurance status, and education.44 It should be noted that the for-profit hospitals in the sample tended to be smaller (M = 159) than the not-for-profit facilities (M = 312). The improved outcomes in this cohort may be due to the fact that for-profit facilities typically do not align with teaching hospitals that are known to take higher-risk clientele and uninsured or under-insured patients. Likewise, for-profit hospitals differ in significant ways relating to operating characteristics, financial strategies, and service constraints.45



Processes are the tasks done for and to patients during the course of care, including both the technical and interpersonal aspects.12 The ability of the L&D nurse to provide the level of care needed for each patient is, to a large extent, driven by the number of nurses available and working, which in part may be influenced by system characteristics (eg, Donabedian proposed that processes are constrained by the structures in which they operate). Nurses play a crucial role in the national agenda to reduce error and promote patient safety because they are present most continuously with the patient and have long been recognized as patient advocates.46


Because the number of nurses may ultimately influence perinatal outcomes, nursing staffing levels must be considered and are designated as the process in the proposed model. This would be measured by the productive nursing care hours (the number of hours worked by all RNs, LPNs, and UAPs with direct patient care responsibilities that are included in the staffing matrix) and the skill mix. The measurement for skill mix would be determined by the percentage of RN nursing care hours from total nursing care hours, the percentage of LPN nursing care hours from total nursing care hours, and the percentage of UAP care hours divided by the total nursing care hours. Both nursing care hours and skill mix could then be calculated for specified time periods (eg, quarterly) and matched with the maternal and baby outcomes for that same time.


To capture the intensity of nursing care and patient acuity through the course of a 24-hour period for the OB population, it is proposed that the total patient hours be calculated as follows: total patient hours = number of deliveries x the standardized nursing workload allotted to each hospital delivery (which generally ranges from 17 hours per delivery to 25 hours per delivery) +/- labor evaluations or patient observations not subsequently admitted (actual time of observation, coded in 1/2-hour increments but converted to hours in analysis) +/- L&D operating room time, which includes all surgical procedures performed in L&D (coded in 1/2-hour increments but converted to hours in analysis) +/- any off-unit monitoring that requires L&D personnel, but not an L&D admission (eg, L& D RN sent to the emergency department to monitor a pregnant woman being evaluated for non-OB-related condition, such as a motor vehicle accident)/24 (Figure 2).

Figure 2 - Click to enlarge in new windowFigure 2. Proposed calculation of total patient hours for intrapartum care.

Given that there is no comparable approach for determining nurse staffing in the perinatal population (other than total nursing hours per patient day, which, as previously described, is inadequate for this group), this approach can provide a standardized measurement of nursing productivity, skill mix, and workload intensity that could be used to measure and evaluate maternal and newborn outcomes on the basis of nursing workload and patient activity.



The final component of Donabedian's model is outcomes, which refer to the consequences of the care provided and are influenced by the patient's interaction with the healthcare system. Although outcome measures do not completely address the entirety of an organization's effectiveness, many patient outcomes (such as morbidity, mortality, access to care, cesarean rates, and medication errors) are considered proxy measures for organizational performance. Outcome measures in the proposed model include cesarean section rates, instrument-assisted vaginal births, NICU admissions, and injuries to the neonate. Kane35 noted the need to account for potential influences of patient and organizational characteristics when studying nurse staffing and patient outcomes. This could be accomplished by controlling for maternal risk factors (including only low-risk, term primiparous, term gestation women in the sample) and hospital characteristics (taking into account teaching status, number of licensed beds, and ownership), which are believed to influence outcomes such as morbidity, mortality, and cesarean section rates.42,47,48 Because it is unreasonable to conduct an experimental design given the nature of the topic, a descriptive design would allow the investigation of possible relationships in staffing and perinatal outcomes and would add to the existing body of knowledge on the extent to which staffing contributes to adverse maternal and neonatal events.


In testing this model, it is important to know whether the outcome measures have been validated in previous studies as indicators of patient safety. In 2006, Grobman et al49 used the 2001 Illinois Department of Public Health's nonfederal hospitals' administrative databases to determine whether the PSIs by AHRQ were significantly affected by patient-specific and hospital characteristics that were not related to the safety environment. In other words, they wanted to determine whether the PSIs represented a changeable adverse event that reflected patient safety or whether the indicators were influenced to a greater extent by either patient characteristics (such as maternal risk factors, gestational age, and ethnicity,) or hospital characteristics (eg, bed size, OB volume). They evaluated more than 175000 births from 142 hospitals and supported that these indicators were a valid measure of health and safety, providing that (a) coding in the hospitals were reliable and accurate, (b) patient and hospital characteristics were accounted for (supported in this model by controlling for hospital characteristics and heterogeneity in the childbearing women), and (c) adverse events that could have decreased frequency with improvements in clinical care were used. They also concluded that the PSIs for OB trauma (third-degree and fourth-degree lacerations, and injury to the pelvic organs) were not an adequate reflection of patient safety and proposed that future OB patient safety measures could include such outcomes as near-miss maternal mortality or process measurements (eg, do staff use appropriate prophylaxis for patients presenting with high-risk conditions?)



A model is proposed for the measurement and evaluation of L&D staffing on maternal and newborn outcomes by using Donabedian's SPO as the theoretical framework. In operationalizing the constructs, it is suggested that the organizational structure be measured by the proxy variables of hospital ownership, teaching status, and bed size. Process variables (referring to the processes germane to patient care) would be measured through nurse staffing and skill mix, and healthcare outcomes for low-risk, primiparous women would be measured through proxy variables of cesarean section rates, instrument-assisted vaginal births, NICU admissions, and injuries to the neonate. Because previous research has validated outcome measures as an appropriate determinant of patient safety in other patient populations, this will allow the opportunity to evaluate OB staffing and its relationship to safe perinatal outcomes for the first time.


A pilot study is currently under way at a large, 449-bed county-owned tertiary-teaching hospital with 5000 births a year to test the model and determine its utility in evaluating the possible links between nurse staffing and perinatal outcomes. The hospital has a very high risk birthing population, with 97% of the women insured through Arizona's Medicaid program (Arizona Healthcare Cost Containment System), 87% Hispanic, and 74% without a high school diploma or equivalent. 44 This ethnically diverse group will provide the ideal forum for pilot testing, with additional cites planned (anticipated 10 hospitals will ultimately be included for analysis).



Donabedian's SPO model provides the ideal framework to measure the influence of staffing and skill mix in L&D to selected adverse OB outcomes. Knowledge of best-practice staffing levels may prove significant because Americans grapple with the cost of providing appropriate healthcare and the implications of healthcare reform. As noted by Kane,35 while there may not be a strong basis to assume that quality of care is directly associated with the level of staffing, it seems unlikely that it is inversely correlated.


It is recognized that hospitals carry a significant burden in the prevention of adverse outcomes that range from nurse staffing levels to the internal process and infrastructure of the hospital setting. Patient outcomes are a direct result not only of the patient's health status and characteristics (eg, socioeconomic position and ethnicity), but also of interactions with the healthcare delivery system. As such, the opportunity to examine hospital characteristics (structure and processes) that may be detrimental to safe maternal and newborn outcomes is of paramount importance in providing optimal outcomes for childbearing women and their families.




1. Needleman J, Buerhaus P, Mattke S. Nurse Staffing Levels and Patient Outcomes in Hospitals. (Final Report). Washington, DC: Health Resources and Services Administration. 2001:230-99-0021. [Context Link]


2. Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K. Nurse-staffing levels and the quality of care in hospitals. N Engl J Med. 2002; 346(22):1715-1722.


3. Cho SH, Ketefian S, Barkauskas VH, Smith DG. The effects of nurse staffing on adverse events, morbidity, mortality, and medical costs. Nurs Res. 2003; 52(2):71-79.


4. Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002; 288(16):1987-1993.


5. Kovner C, Gergen PJ. Nurse staffing levels and adverse events following surgery in US hospitals. Image J Nurs Sch. 1998; 30(4):315-321.


6. McCue M, Mark BA, Harless DW. Nurse staffing, quality, and financial performance. J Health Care Finance. 2003; 29(4):54-76. [Context Link]


7. Phibbs CS, Bronstein JM, Buxton E, Phibbs RH. The effects of patient volume and level of care at the hospital of birth on neonatal mortality. JAMA. 1996; 276(13):1054-1059. [Context Link]


8. Tarnow-Mordi WO, Hau C, Warden A, Shearer AJ. Hospital mortality in relation to staff workload: a 4-year study in an adult intensive-care unit. Lancet. 2000; 356(9225):185-189. [Context Link]


9. Field D, Hodges S, Mason E, Burton P. Survival and place of treatment after premature delivery. Arch Dis Child. 1991; 66(4)(Spec No.):408-410. [Context Link]


10. Field D, Draper E. Survival and place of delivery following preterm birth: 1994-96. Arch Dis Child Fetal Neonatal Ed. 1999; 80(2):F111-F114. [Context Link]


11. Tucker J, UK Neonatal Staffing Study Group. Patient volume, staffing, and workload in relation to risk-adjusted outcomes in a random stratified sample of UK neonatal intensive care units: a prospective evaluation. Lancet. 2002; 359(9301):99-107. [Context Link]


12. Donabedian A. Methods for deriving criteria for assessing the quality of medical care. Med Care Rev. 1980; 37(7):653-698. [Context Link]


13. Chou AF, Norris AI, Williamson L, Garcia K, Baysinger J, Mulvihill JJ. Quality assurance in medical and public health genetics services: a systematic review. Am J Med Genet C Semin Med Genet. 2009; 151C(3):214-234. [Context Link]


14. Chou AF, Yano EM, McCoy KD, Willis DR, Doebbeling BN. Structural and process factors affecting the implementation of antimicrobial resistance prevention and control strategies in US hospitals. Health Care Manage Rev. 2008; 33(4):308-322.


15. Hong SI, Morrow-Howell N, Proctor E, Wentz JD, Rubin E. The quality of medical care for comorbid conditions of depressed elders. Aging Ment Health. 2008; 12(3):323-332.


16. Kunkel S, Rosenqvist U, Westerling R. The structure of quality systems is important to the process and outcome, an empirical study of 386 hospital departments in Sweden. BMC Health Serv Res. 2007; 7:104.


17. Liebel DV, Friedman B, Watson NM, Powers BA. Review of nurse home visiting interventions for community-dwelling older persons with existing disability. Med Care Res Rev. 2009; 66(2):119-146.


18. Qu H, Shewchuk RM, Chen YY, Richards JS. Evaluating the quality of acute rehabilitation care for patients with spinal cord injury: an extended Donabedian model. Qual Manag Health Care. 2010; 19(1):47-61. [Context Link]


19. Gagnon AJ, Meier KM, Waghorn K. Continuity of nursing care and its link to cesarean birth rate. Birth. 2007; 34(1):26-31. [Context Link]


20. Radin TG, Harmon JS, Hanson DA. Nurses' care during labor: its effect on the cesarean birth rate of healthy, nulliparous women. Birth. 1993; 20(1):14-21. [Context Link]


21. Hodnett ED, Gates S, Hofmeyr GJ, Sakala C. Continuous support for women during childbirth. Cochrane Database Syst Rev. 2007;(3):CD003766. [Context Link]


22. Hodnett ED, Lowe NK, Hannah ME, et al. Effectiveness of nurses as providers of birth labor support in North American hospitals: a randomized controlled trial. JAMA. 2002; 288(11):1373-1381. [Context Link]


23. Zinberg S. The ethics of elective cesarean, ACOG responds. The Washington Post. 2004:A27. [Context Link]


24. Liu S, Liston RM, Joseph KS, et al. Maternal mortality and severe morbidity associated with low-risk planned cesarean delivery versus planned vaginal delivery at term. CMAJ 2007; 176(4):455-460. [Context Link]


25. MacDorman MF, Menacker F, Declercq E. Cesarean birth in the United States: epidemiology, trends, and outcomes. Clin Perinatol. 2008; 35(2):293-307. [Context Link]


26. National Quality Forum. Cesarean rate for low-risk first birth women (aka NTSV CS rate). NQF 0471. Updated 2009. Accessed November 2009. [Context Link]


27. Agency for Healthcare Research and Quality. Guide to inpatient quality indicators. Published 2007. Accessed August 2009. [Context Link]


28. Agency for Healthcare Research and Quality. Guide to patient safety indicators. . Published 2007. Accessed August 2009. [Context Link]


29. Joint Commission. Joint Commission perinatal core performance measurement. Updated 2009. Accessed November 2009. [Context Link]


30. Rothberg MB, Abraham I, Lindenauer PK, Rose DN. Improving nurse-to-patient staffing ratios as a cost-effective safety intervention. Med Care. 2005; 43(8):785-791. [Context Link]


31. Joint Commission on Accreditation of Healthcare Organizations. Health Care at the Crossroads: Strategies for Addressing the Evolving Nursing Crisis. Updated 2010. Accessed May 2010. . [Context Link]


32. Jones KR, Burney RE. Outcomes research: an interdisciplinary perspective. Outcomes Manag. 2002; 6(3):103-109. [Context Link]


33. Elting LS, Pettaway C, Bekele BN, et al. Correlation between annual volume of cystectomy, professional staffing, and outcomes: a statewide, population-based study. Cancer. 2005; 104(5):975-984. [Context Link]


34. Halm M, Peterson M, Kandels M, et al. Hospital nurse staffing and patient mortality, emotional exhaustion, and job dissatisfaction. Clin Nurse Spec. 2005; 19(5):241-251.


35. Kane RL, Shamliyan TA, Mueller C, Duval S, Wilt TJ. The association of registered nurse staffing levels and patient outcomes: systematic review and meta-analysis: see comment. Med Care. 2007; 45(12):1195-1204. [Context Link]


36. Mark BA, Harless DW, McCue M, Xu Y. A longitudinal examination of hospital registered nurse staffing and quality of care. Health Serv Res. 2004; 39(2):279-300. [Context Link]


37. Aiken LH. Economics of nursing. Policy Polit Nurs Pract. 2008; 9(2):73-79. [Context Link]


38. Mark W, Stanton MA. Hospital nurse staffing and quality of care. Prof Inferm. 2005; 58(2):67-74. [Context Link]


39. Estabrooks CA, Midodzi WK, Cummings GG, Ricker KL, Giovannetti P. The impact of hospital nursing characteristics on 30-day mortality. Nurs Res. 2005; 54(2):74-84. [Context Link]


40. Landon BE, Normand SL, Lessler A, et al. Quality of care for the treatment of acute medical conditions in US hospitals. Arch Intern Med. 2006; 166(22):2511-2517.


41. McCloskey BA, Diers DK. Effects of New Zealand's health reengineering on nursing and patient outcomes. Med Care. 2005; 43(11):1140-1146. [Context Link]


42. Clark SL, Xu W, Porter TF, Love D. Institutional influences on the primary cesarean section rate in Utah, 1992 to 1995. Am J Obstet Gynecol. 1998; 179(4):841-845. [Context Link]


43. Oleske DM, Glandon GL, Giacomelli GJ, Hohmann SF. The cesarean birth rate: influence of hospital teaching status. Health Serv Res. 1991; 26(3):325-337. [Context Link]


44. Wilson B. The Influence of Hospitals, Providers, and Patients in Birth Outcomes Following Induction of Labor [Dissertation]. Tucson: University of Arizona; 2008. [Context Link]


45. Miller P, Yasin MM, Zimmerer TW. An empirical investigation of quality improvement initiatives in for-profit and not-for-profit hospitals: Environmental, competitive and outcome concerns. Int J Health Care Qual Assur Inc Leadersh Health Serv. 2006; 19(6-7):539-550. [Context Link]


46. Benner P, Sheets V, Uris P, Malloch K, Schwed K, Jamison D. Individual, practice, and system causes of errors in nursing: a taxonomy. J Nurs Adm. 2002; 32(10):509-523. [Context Link]


47. Mitchell PH, Heinrich J, Moritz P, Hinshaw AS. Outcome measures and care delivery systems. Introduction and purposes of conference. Med Care. 1997; 35(11)(suppl):NS1-NS5. [Context Link]


48. Spetz J, Smith MW, Ennis SF. Physician incentives and the timing of cesarean sections: evidence from California. Med Care. 2001; 39(6):536-550. [Context Link]


49. Grobman WA, Feinglass J, Murthy S. Are the agency for healthcare research and quality obstetric trauma indicators valid measures of hospital safety? Am J Obstet Gynecol. 2006; 195(3):868-874. [Context Link]


nurse staffing; patient safety; perinatal outcomes