Twelve tips for how institutional ethnography (IE) is conducted in health professions education research
Bibliographic record
Abstract
Institutional ethnography (IE), a term coined by sociologist Dorothy Smith, explores the nuances of institutions and their complex relationships in sociology. IE is an approach to studying and analysing social organization, and it provides a more holistic understanding of 'invisible' relationships that govern institutions and how those relationships interact with each other. Health sciences researchers in patient care, patient experience, and allied health professionals have recently become more interested in the use of this methodology and how to incorporate it into their research. However, in health professions education (HPE) there is little use of IE. We hypothesize this may be because of limited practical knowledge of this methodology. This paper serves as an introduction to the use of IE in HPE, describing the differences between IE and traditional ethnographies, recognizing the common pitfalls when utilising IE, and incorporating texts into IE. While ethnographies may be daunting to researchers less familiar with these approaches, the tips in this paper will provide an introduction and help educators and researchers successfully navigate the use of IE in health profession scholarship and education.
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How this classification was reachedexpand
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 | 0.056 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".