Complexity in dynamical health systems – transforming science and theory, and knowledge and practice
Bibliographic record
Abstract
The reason is that in order to see the world in a new way you have to step out of the traditional frame and into a new one. Once done, you can never go back. The ability to reframe a question is the basis for change and broadening of ideas 1. Complexity thinking is slowly emerging in health systems, while the transdisciplinary fields of complexity science and theory are developing at a much faster pace. A dictionary of complexity comprising 11 volumes of state of the art theory and knowledge has been published as a reference book, yet only two papers relate to health in this epic publication 2. Peter Erdi, in another but more accessible reference publication, illuminates how complex collective behaviour emerges from the parts of a system, because of the interaction between the system and its environment 3. Very different complex phenomena of nature and society can be analysed and understood by non-linear dynamics because many systems of very different fields, that impact on human health and medical care such as physics, chemistry, biology, economics, psychology and sociology, etc. have similar architecture 3. Examples of mathematical modelling of complex systems related to the health of the body and mind, and health care and human behaviour are beginning to appear in many fields. In this edition of the Forum, Katerndal 4 models doctor behaviour in consultations involving uncontrolled diabetes using symbolic dynamics in categorical time series data. The identification of the amount of complexity and structure present and recurrent patterns associated with change in medication has implications for improving practice. Nevertheless, most of current complexity activity in health and medical care has been theory-based using the organizing principles of complex adaptive systems to frame and intervene in systems in the real world. In this edition, Gell focuses on the macro health system and analyses the carbon cycle involved in ‘producing’ heath care in its current social construction and policy directions 5. He explores the evolution of the health care enterprise in terms of response to the combined challenges of climate change and scarcity of natural resources with implications for the step change taking place across the global economy. Closer to everyday health services are three papers that are about political and sometimes controversial aspects of health care philosophy in relation to health care organizations and practice. Jordon, Lanham, Anderson and McDaniel draw on findings from their research to suggest the usefulness of theories of complex adaptive systems in guiding research interpretation 6. They address the implications for interpreting research observations in health care organizations in relation to observing relationships among diverse agents and among agents that can inform the study of health service delivery. The paper by Downe 7 is a philosophical and conceptual analysis of the complex and sometimes conflicted and confused arena of childbirth and where the translation of formally condoned ‘evidence’ into practice has been highly problematic internationally. Martin and Kaspersky 8 present a real world case study of dealing with the philosophy, vision and practical issues when key provider groups, recognizing the limitations of linear research, convened to reach a working consensus about how to resolve workforce and professional barriers to collaboration improve care for mothers, babies and families in Ontario. In a sense, this paper seeks to apply many of the principles and methods of complex adaptive systems expounded by Jordan et al.6 and the analysis of Downe 7 in the process of health system transformation. Thus in complexity in relation to health systems – we have approaches ranging from mathematical modelling to sophisticated thought experiments and analysis to case studies of real world activities. While complexity-informed research has not yet transformed the mainstream, and is often surrounded by mystique or the notion that complexity is a fad, this forum is demonstrating the depth of the field and why it deserves greater attention and more serious study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".