The effects of education and clinical specialization on nurses’ status affirmation by physicians: A quantitative analysis

ABSTRACT

Research has demonstrated a status gap between members of healthcare delivery teams. However, it is unclear which factors mitigate or exacerbate the status gap between healthcare providers. This paper examines the concept of status affirmation, the belief that others affirm the individual’s social standing, as one factor that can affect the status gap between healthcare professionals. The aim of this exploratory study was to investigate two factors that affect nurses’ status affirmation: nurses’ educational backgrounds and clinical specializations. A close-ended survey was administered to registered nurses in Indiana, a midwestern American state¹ (N = 1262) to identify which nurses are likely to have their status affirmed by physicians, in general. Results of multinomial logistic regression analyses suggest that highly educated nurses are unlikely to receive status affirmation, and there are differences in status affirmation across clinical specialties. In addition, nurses with advanced degrees often do not work in specialties that receive status affirmation. These results suggest that conflict among nurses and doctors is as likely to exist across divisions in nurses’ educational attainment as across work specializations. Status affirmation is posited as a theoretical antecedent to interprofessional collaboration.

KEYWORDS:

• Quantitative method
• interprofessional collaboration
• interprofessional relations
• power
• roles

Acknowledgments

The authors would like to acknowledge the following people for their helpful feedback: Bernice Pescosolido, Tim Hallett, Roberta Lessor, Orla Stapleton, Art Alderson, Eliza Pavalko, Milisa Manojlovich, Emma Frieh, and Brea Perry.

Disclosure Statement

Professor Rojas has received remuneration for speaking at universities, non-profit organizations, and for-profit firms on various topics.

Notes

1. Aspiring nurses in America can become RNs by completing an accredited nursing training program, usually affiliated with an institution of higher education. Two-year associate degrees, four-year Bachelor of Science degrees, and three-year diploma programs can be accredited. After satisfactorily passing their coursework in an accredited program, prospective nurses must take an RN certification test (NCLEX, offered through the National Council of State Boards of Nursing). A passing test score becomes the permit for a license from a specific state. After obtaining their license, nurses can work in many specialties including, but by no means limited to, surgery, intensive care, obstetrics, outpatient nursing, educators, etc. If nurses wish to specialize in advanced practice (e.g., nurse practitioners, certified nurse midwives, etc.), they require postgraduate education.

2. The p-values for the Wald test were as follows: combining the nurses who disagree that doctors think of nurses as equals with the nurses who were unsure about what doctors think of them (p = .063), combining the nurses who were unsure about what doctors think of them with the nurses who agree that doctors think of nurses as equals (p = .023), combining the nurses who disagreed that doctors think of nurses as equals with the nurses who agreed (p < .001). At the .10 level, none of the null hypotheses, which state that the categories could be combined, can be rejected. Therefore, we retained the three categories.

3. There is good reason to expect some variation between RNs with a bachelor’s degree and nurses with lower levels of education such as LPNs. However, our sampling frame included only RNs.

4. We also performed the statistical tests using ordinal logistic regressions. In the ordinal model, the odds ratios are much the same as the odds ratios in the multinomial model. However, ordinal regressions impose an ordering on the categories of the dependent variable (Long & Freese, 2014). In ordinal models, the distribution of the nurses who disagree that doctors of nurses as equals must overlap with the distribution of nurses who are uncertain about what doctors think of them, the distribution of which must overlap the distribution of the nurses who agree that doctors think of nurses as equals. Multinomial regressions relieve these constraints and allow the distributions to form as they appear in the data. Though multinomial regressions have more coefficients to interpret because of the many comparisons, it is the model that least constrains the data and most closely models empirical reality. The Average Marginal Effects plots in Appendix B show how the probabilities differ when the categories of the dependent variable are constrained in the ordinal model and not constrained in the multinomial model.

5. It may be that scope of practice laws affect the relationship between nurses and doctors. Perhaps advanced practice nurses in states with restrictive scope of practice laws feel unnecessarily supervised by physicians, and therefore they report that doctors do not think of them as equals. In Indiana, scope of practice laws permit nurse practitioners and other advanced practice nurses to engage in reduced practice. They must establish a career-long collaborative agreement with a supervisory physician, but their ability to practice is reduced in at least one area of advanced nursing practice. This in contrast to states that allow full practice, where advanced practice nurses can autonomously evaluate patients, diagnose them, interpret tests, and prescribe medicine, and states that restrict practice, where advanced practices nurses must establish a career-long collaborative and managerial relationship with a physician. Because the nurses in our sample come from only one state, we cannot assess how variation in scope of practice laws affect status affirmation. Future research with larger samples could investigate this relationship.

Additional information

Funding

This research was supported by a Indiana University’s Clinical and Translational Studies Institute [PDT 410]