Background & Aims
Understanding, measuring, and mitigating pain-related suffering is a key challenge for both clinical care and pain research. However, there is no consensus on what exactly the concept of pain-related suffering includes, and it is often not precisely operationalized in empirical studies. Here, we 1.) systematically review the conceptualization of pain-related suffering in the existing literature, 2) develop a definition and a conceptual framework, and 3.) use machine learning to cross-validate the results.
Methods
We identified 111 articles in a systematic search of Web of Science, Pubmed, PsychInfo and PhilPapers for peer-reviewed articles containing conceptual contributions about the experience of pain-related suffering. We developed a new procedure for extracting and synthesizing study information based on the cross-validation of qualitative analysis (Walker & Avant, 1995) with an artificial intelligence-based approach grounded in Large Language Models (LLM) and Topic Modeling (Blei, Ng, & Jordan, 2003; Brown et al., 2018).
Results
-We derived a definition from the literature that is representative of current theoretical views and describes pain-related suffering as “a severely negative, complex, and dynamic experience in response to a perceived threat to an individual’s integrity as a self and identity as a person”.
-According to the literature, feelings of loss, lack of control, illness, alienation, and reduced quality of life are paradigmatic instances of suffering. However, most authors stress that none of these can in itself be considered suffering, if it is not experienced as a threat as defined above.
-We also offer a conceptual framework of pain-related suffering distinguishing 8 dimensions: social, physical, personal, spiritual, existential, cultural, cognitive, and affective. Each of these dimensions of our framework is further specified by 2-4 key words that were derived directly from the literature.
Conclusions
There is currently no consensus on a definition of pain-related suffering. Important aspects and dimensions of pain-related suffering that are stressed in some parts of the literature, are ignored in others. The present analysis addresses this problem and provides a roadmap for further theoretical and empirical development by offering an integrative definition and conceptual framework.
References
Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of machine Learning research 2003;3(Jan):993-1022.
Brown TBM, Benjamin ; Ryder, Nick ; Subbiah, Melanie ; Kaplan, Jared ; Dhariwal, Prafulla ; Neelakantan, Arvind ; Shyam, Pranav ; Sastry, Girish ; Askell, Amanda ; Agarwal, Sandhini ; Herbert-Voss, Ariel ; Krueger, Gretchen ; Henighan, Tom ; Child, Rewon ; Ramesh, Aditya ; Ziegler, Daniel M. ; Wu, Jeffrey ; Winter, Clemens ; Hesse, Christopher ; Chen, Mark ; Sigler, Eric ; Litwin, Mateusz ; Gray, Scott ; Chess, Benjamin ; Clark, Jack ; Berner, Christopher ; McCandlish, Sam ; Radford, Alec ; Sutskever, Ilya ; Amodei, Dario. Language Models are Few-Shot Learners. Advances in neural information processing systems 2020;33:1877-1901.
Walker LO, Avant KC. Strategies for Theory Construction in Nursing: Appleton & Lange, 1995.
Presenting Author
Niklas Noe-Steinmüller
Poster Authors
Niklas Noe-Steinmüller
MSc, MA
Department of General Internal Medicine and Psychosomatics, Heidelberg University, Heidelberg, Germa
Lead Author
Dmitry Scherbakov
School of Public Health, University of Haifa, Israel
Lead Author
Alexandra Zhuravlyova
School of Public Health, University of Haifa, Israel
Lead Author
Tor Wager
Dartmouth College
Lead Author
Pavel Goldstein
University of Haifa
Lead Author
Jonas Tesarz
University Hospital Heidelberg
Lead Author
Topics
- Systematic Reviews/Meta-Analysis