We Need to Talk About Complexity in Health Research: Findings From a Focused Ethnography
Bibliographic record
Abstract
There is increasing focus on complexity-informed approaches across health disciplines. This attention takes several forms, but commonly involves framing research topics as "complex" to justify use of particular methods (e.g., qualitative). Little emphasis is placed on how divergent and convergent ways of knowing complexity become negotiated within academic communities. Drawing on findings from a focused ethnography of an international workshop, we illustrate how health researchers employ "boundary-ordering devices" to navigate different meanings ascribed to complexity while they attempt to sustain interdisciplinary communication and collaboration. These include (a) surfacing (but not resolving) tensions between philosophical grounding of knowledge claims and need for practical purchase, (b) employing techniques of representation and abstraction, and (c) drawing on the fluid, ongoing accomplishment of complexity for different audiences and purposes. Our findings have implications for progressing complexity-informed health research, particularly with respect to qualitative approaches.
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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 | 0.058 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".