Sonia Semenic is an Associate Professor at the Ingram School of Nursing, McGill University (Montreal, Quebec, Canada) and a Nurse Scientist at the McGill University Health Center. After many years of experience as an IBCLC and Clinical Nurse Specialist in maternal-child health, Sonia completed a PhD in Nursing and postdoctoral training in community health. Her research aims to better understand the process of knowledge translation (KT) in perinatal health, with a particular focus on the implementation of evidence-based practices to protect, promote and support breastfeeding. She currently co-leads the Knowledge Translation Platform for the Quebec Nursing Intervention Research Network, and teaches graduate courses on knowledge translation in nursing practice.
Despite irrefutable research evidence for the benefits of breastfeeding, less than 37% of infants worldwide meet WHO targets for optimal breastfeeding. Persistently low breastfeeding rates are due in part due to poor uptake of breastfeeding best-practice guidelines, such as the Baby-Friendly Hospital Initiative. The growing field of knowledge translation in healthcare reveals that it takes from 8-30 years for research findings to be adopted into clinical practice, and that up to 45% of patients don’t receive evidence-based healthcare. This presentation aims to help those providing lactation support to better understand the complexity of factors influencing the use of evidence in practice, as well as what can be done to facilitate the uptake of best practice guidelines to protect, promote and support breastfeeding. Whether or not care providers follow evidence-based practices is influenced by the nature of the evidence (e.g., perceived relevance of the evidence), characteristics of the care providers (e.g., motivations to change practice) as well as characteristics of the care environment (e.g., leadership support for change). Successful strategies for supporting practice change are tailored to local barriers and facilitators to evidence use, and can be informed by the growing number of theoretical models and frameworks for KT in healthcare.
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