Taking sides with patients using institutional ethnography
Bibliographic record
Abstract
Purpose The main purpose of this paper is to document the first author's experience of using institutional ethnography (IE) to “take sides” in healthcare research. The authors illustrate the points with data and key findings from a study of cardiovascular disease prevention. Design/methodology/approach The authors use Dorothy E Smith's IE approach, and particularly the theoretical tool of “standpoint”. Findings Starting with the development of the study, the authors trouble the researcher's positionality, highlighting tensions between institutional knowledge of “prevention” and other locations where knowledge about patients' health needs materialises. The authors outline how IE's theoretically and methodologically integrated toolkit became a framework for “taking sides” with patients. They describe how the researcher used IE to take a standpoint and map institutional relations from that standpoint. They argue that IE enabled an innovative analysis but also reflect on the challenges of conducting an IE – the conceptual unpicking and (re)thinking, and demarcating boundaries of investigation within an expansive dataset. Originality/value This paper illustrates IE's relevance for organisational ethnographers wishing to find a theoretically robust approach to taking sides, and suggests ways in which the IE approach might contribute to improving services, particularly healthcare. It provides an illustration of how taking a patient standpoint was accomplished in practice, and reflects on the challenges involved.
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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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".