Developing scalable strategies for early essential newborn care: A network-driven qualitative analysis.

Area of Science:

• Healthcare implementation science
• Neonatal health outcomes
• Maternal-newborn care practices

Background:

• The World Health Organization's early essential newborn care (EENC) package is vital for reducing neonatal mortality.
• A significant gap exists between EENC evidence and real-world clinical practice.

Purpose of the Study:

• To analyze EENC implementation in China using two analytical approaches.
• To identify barriers and facilitators impacting EENC adoption.
• To propose strategies for effective EENC scaling and promotion.

Main Methods:

• A descriptive qualitative study involving semi-structured interviews with 33 healthcare professionals in China.
• Data analysis using deductive thematic coding based on the Consolidated Framework for Implementation Research (CFIR).
• Computational network analysis to examine factor interactions and identify pivotal strategies.

Main Results:

• Identified 11 barriers (e.g., safety concerns, lack of standards, resource limits) and 39 facilitators (e.g., knowledge gain, leadership support).
• Prominent barriers included provider safety concerns, inconsistent standards, and resource constraints.
• Key facilitators involved knowledge acquisition, positive belief shifts, and organizational support.

Conclusions:

• EENC implementation is influenced by complex, interrelated factors.
• Four high-leverage strategies proposed: initiating EENC, securing leadership support, establishing unified standards, and continuous feedback/quality control.
• These strategies offer a practical framework to overcome challenges and improve newborn and maternal outcomes.