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State-level scope of practice regulations for nurse practitioners impact work environments: Six state investigation.

Lusine PoghosyanJordan H SteinJianfang LiuJoanne SpetzZainab Toteh OsakweGrant Martsolf
Published in: Research in nursing & health (2022)
Nurse practitioner (NP) scope of practice (SOP) policies are different across the United States. Little is known about their impact on NP work environment in healthcare organizations. We investigated the association between SOP policies and organizational-level work environment of NPs. Through a cross-sectional survey design, data were collected from 1244 NPs in six states with variable SOP regulations (Arizona, New Jersey, Washington, Pennsylvania, Texas, and California) in 2018-2019. Arizona and Washington had full SOP-NPs had full authority to deliver care. New Jersey and Pennsylvania had reduced SOP with physician collaboration requirement; California and Texas had restricted SOP with physician supervision requirement. NPs completed mail or online surveys containing the Nurse Practitioner Primary Care Organizational Climate Questionnaire, which has these subscales: NP-Administration Relations (NP-AR), NP-Physician Relations (NP-PR), Independent Practice and Support (IPS), and Professional Visibility (PV). Regression models assessed the relationship between state-level SOP and practice-level NP work environment. NP-AR scores were higher in full SOP states compared to reduced (β = 0.22, p < 0.01) and restricted (β = 0.15, p < 0.01) SOP states. Similarly, IPS scores were higher in full SOP states. The PV scores were also higher in full SOP states compared to reduced (β = 0.16, p < 0.001) and restricted (β = 0.12, p < 0.05) SOP states. There was no relationship between SOP and NP-PR score. State-level policies affect NP work environment. In states with more favorable policies, NPs have better relationships with administration and report more role visibility and support. Efforts should be made to remove unnecessary SOP restrictions.
Keyphrases
  • primary care
  • healthcare
  • public health
  • quality improvement
  • emergency department
  • general practice
  • machine learning
  • deep learning
  • high speed