The field of implementation science is a fascinating realm that continually evolves and expands. As I navigate this dynamic landscape, I find it both stimulating and challenging to keep up with the latest research, emerging trends, and valuable insights that can enhance my work as a researcher or practitioner. However, I’m excited to share an article by Dr. Victoria Scott and myself that explores these trends and introduces a groundbreaking tool that can revolutionize our understanding of this vast field.
In 5335 days of Implementation Science: using natural language processing to examine publication trends and topics, we took a deep dive into the intricacies of publication trends within implementation science. We shed light on key topics that have emerged and evolved over the years, all while emphasizing the innovative application of frankly pretty basic Natural Language Processing (NLP) techniques to synthesize and translate a significant volume of the published content.
Staying Current with Implementation Science
We looked at trends in trends in articles published in the field of implementation science from 2006 to 2020. We found that there was an emergent emphasis on systematic reviews and discussed the shift away from the term “knowledge translation.” The study provides valuable insights into pivotal areas such as HIV, stroke, and health equity, which hold significant importance within the realm of implementation science.
Exploring the Power of NLP
The second part of the article focuses on the potential of NLP as a tool to identify trends and topics across many articles. With the increasing volume of academic publications, keeping pace with the latest research literature has become increasingly challenging. Here is where NLP comes to the rescue, offering a more efficient and effective method for synthesizing and translating scientific literature. However, we emphasize that NLP does not replace the need for deep engagement with articles. Instead, they propose it as an additional tool to assist seasoned researchers and practitioners in navigating the vast scientific literature.
Interestingly, despite its growing importance in health-related research, we DID NOT find the term “health equity” within any topic cluster. This finding suggests a need for more equity-specific research within the field.
For the future, we advocate for an approach incorporating deep semantic modeling and word vectorization models relying on data-rich inputs. We can utilize recent progress in advanced language models that produce accurate, actionable, and ethically sound results. And the soon-to-be-relaunched PubTrawlr uses many of these methods.
Why You Should Consider Reading It
This article is a nice resource for anyone involved in implementation science or seeking a deeper understanding of the field. Furthermore, introducing NLP as a tool for analyzing a large volume of published content may inspire you to explore innovative methodologies in your own research or practice.
Embark on a captivating journey through the layers of implementation science and discover how NLP has the potential to transform your understanding of this complex field. I encourage you to read the full article here.