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Record W2897580755 · doi:10.1111/medu.13651

What is the state of complexity science in medical education research?

2018· article· en· W2897580755 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Education · 2018
Typearticle
Languageen
FieldComputer Science
TopicChaos, Complexity, and Education
Canadian institutionsWomen's and Gender Studies et Recherches FéministesWestern University
Fundersnot available
KeywordsCitationVariety (cybernetics)Field (mathematics)SimplicityDisciplineScience educationHealth scienceComplexity scienceData scienceComputer scienceSociologyEpistemologyPsychologyMathematics educationSocial scienceManagement scienceMedicineMathematicsLibrary scienceMedical educationArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.106
GPT teacher head0.448
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it