What is the state of complexity science in medical education research?
Bibliographic record
Abstract
CONTEXT: 'Complexity' is fast becoming a 'god term' in medical education, but little is known about how scholars in the field apply complexity science to the exploration of education phenomena. Complexity science presents both opportunities and challenges to those wishing to adopt its approaches in their research, and debates about its application in the field have emerged. However, these debates have tended towards a reductive characterisation of complexity versus simplicity. We argue that a more productive discussion centres on the multiplicity of complexity orientations, with their diverse disciplinary roots, concepts and terminologies. We discuss this multiplicity and use it to explore how medical education researchers have taken up complexity science in prominent journals in the field. METHODS: We synthesised the health sciences and medical education literature based on 46 papers published in the last 18 years (2000-2017) to describe the patterns of use of complexity science in medical education and to consider the consequences of those patterns for our ability to advance scholarly conversations about 'complex' phenomena in our field. RESULTS: We identified four patterns in the use of complexity science in medical education research. Firstly, complexity science is described in a variety of ways. Secondly, multiple approaches to complexity are used in combination in single papers. Thirdly, the type of complexity science used tends to be left implicit. Fourthly, the complexity orientation used is much more commonly located using secondary source citation rather than primary source citation. CONCLUSIONS: The presence of these four patterns begs the question: Do medical education scholars understand that there are multiple legitimate orientations to complexity science, deriving from distinct disciplinary origins, drawing on different metaphors and serving distinct purposes? If we do not understand this, a cascade of potential consequences awaits. We may assume that complexity science is singular in that there is only one way to do it. This assumption may cause us to perceive our way as the 'right' way and to disregard other approaches as illegitimate. However, this perception of illegitimacy may limit our ability to enter into productive dialogue about our complexity science-inspired research.
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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 | 0.129 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.040 | 0.052 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".