Actor-network theory and ethnography: Sociomaterial approaches to researching medical education
Bibliographic record
Abstract
Medical education is a messy tangle of social and material elements. These material entities include tools, like curriculum guides, stethoscopes, cell phones, accreditation standards, and mannequins; natural elements, like weather systems, disease vectors, and human bodies; and, objects, like checklists, internet connections, classrooms, lights, chairs and an endless array of others.We propose that sociomaterial approaches to ethnography can help us explore taken for granted, or under-theorized, elements of a situation under study, thereby enabling us to think differently. In this article, we describe ideas informing Actor-Network Theory approaches, and how these ideas translate into how ethnographic research is designed and conducted. We investigate epistemological (what we can know, and how) positioning of the researcher in an actor-network theory informed ethnography, and describe how we tailor ethnographic methods-document and artefact analysis; observation; and interviews-to align with a sociomaterial worldview.Untangling sociomaterial scenarios can offer a novel perspective on myriad contemporary medical education issues. These issues include examining how novel tools (e.g. accreditation standards, assessment tools, mannequins, videoconferencing technologies) and spaces (e.g. simulation suites, videoconferenced lecture theatres) used in medical education impact how teaching and learning actually happen in these settings.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".