Critical ethnography: implications for medical education research and scholarship
Bibliographic record
Abstract
CONTEXT: Medical education (ME) must rethink the dominant culture's fundamental assumptions and unintended consequences on less advantaged groups and society at large. Doing so, however, requires a robust understanding of what we are teaching, regardless of our intentions, and what is being learned across the multiple settings that our learners find themselves in, from classrooms to clinical spaces and beyond. APPROACH: Gaining such understandings and fully exploring the extent to which we are rising to the challenges of today's society in authentic ways require robust methodologies. In this research approaches paper, we introduce unfamiliar readers to one such methodology-critical ethnography. By doing so, we hope to demonstrate its potential for helping ME both identify and gain novel insight into necessary solutions for many of today's educational challenges regarding healthcare disparities and inequities. CONCLUSION: The readers of this paper will gain novel insights into how critical ethnographers see the world and ask questions, thereby changing the way they (the reader) see the world. At its heart, critical ethnography is about thinking differently and that is something that should be accessible to all. Doing so may also enhance our ability to both question dominant ways of thinking and, ultimately, to enact positive change in training and practices to enhance inclusivity and fairness for all regardless of their gender, race and status.
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.
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 | 0.169 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".