Blurring the boundaries: using institutional ethnography to inquire into health professions education and practice
Bibliographic record
Abstract
CONTEXT: Qualitative, social science approaches to research have surged in popularity within health professions education (HPE) over the past decade. Institutional ethnography (IE) offers the field another sociological approach to inquiry. Although widely used in nursing and health care research, IE remains relatively uncommon in the HPE research community. This article provides a brief introduction to IE and suggests why HPE researchers may wish to consider it for future studies. METHODS: Part 1 of this paper presents IE's conceptual grounding in: (i) the entry point to inquiry ('materiality'), (ii) a generous definition of 'work' and (iii) a focus on how 'texts' such as policies, forms and written protocols influence activity. Part 2 of this paper outlines the method's key features through exemplars from our own research. Part 3 discusses the ways in which research that blurs the lines between educational and clinical practice can be both generative for HPE and accomplished using IE. RESULTS: The authors demonstrate the usefulness of IE for studying complex social issues in HPE. It is posited that a key added value of IE is that it goes beyond individual-level explanations of problems and phenomena, yet also closely studies individuals' activities, rather than remaining at an abstract or distant level of analysis. Thereby, IE can result in feasible and meaningful social change at the nexus of health professions education and other social systems such as clinical practice. CONCLUSIONS: IE adds to the growing qualitative research toolkit for HPE researchers. It is worth considering because it may enable change through the study of HPE in relation to other social processes, structures and systems, including the clinical practice world. A particular benefit may be found in blending HPE research with research on clinical practice, toward changing practice and policy through IE, given the interrelated nature of these fields.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".