Actor-network theory and ethnography: Sociomaterial approaches to researching medical education
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it