A Phenomenological Study on Nurse Productivity Challenges in a Resource-Limited Primary Hospital in the Philippines - Jose Antonio N Estreller; Jonathan Mark N Fontanilla; Mohammad Sajjad Fakhri; Jesus P Briones; Marmelo V Abante (Advanced Qualitative Research, 2025)
Nurse productivity is critical to health care efficiency, particularly in resource-limited primary hospitals where operational challenges significantly hinder nurses’ ability to deliver optimal care. This research examined the various productivity challenges faced by nurses in a resource-limited primary hospital in the Philippines and identified potential solutions to improve their current working conditions. A phenomenological approach was used to collect data through in-depth interviews of seven registered nurses at a resource-limited primary hospital. Four themes emerged from the thematic analysis, addressing both the challenges and potential solutions to the issues faced by the nurses. The challenges included high workloads and limited staffing, resource scarcity, emotional and mental strain, and training and knowledge gaps. The themes for the proposed solutions include adequate staffing and support resources, consistent access to essential supplies and equipment, skill development and continuous training, and holistic support for nurse well-being and productivity. Based on these findings, it is imperative to offer systemic changes, such as enhanced staffing models, better resource allocation, and strengthened support systems, for nurses to lessen nurse productivity challenges in resource-limited settings. This study provides valuable insights into the complexities of nursing practice in a primary healthcare setting and serves as a foundation for developing targeted strategies to enhance nurse productivity and improve patient care quality within the Philippine healthcare system. Further, this study can serve as a reference for a more focused evaluation of nurse productivity in a resource-limited hospital setting, offering localized insights and context-specific recommendations.