Health Inequity and Institutional Ethnography: Mapping the Problem of Policy Change
Bibliographic record
Abstract
Health equity (HE) is a central concern across multiple disciplines and sectors, including nursing. However, the proliferation of the term has not resulted in corresponding policymaking that leads to a clear reduction of health inequities. The goal of this paper is to use institutional ethnographic methods to map the social organization of HE policy discourses in Canada, a process that serves to reproduce existing relations of power that stymie substantive change in policy aimed at reducing health inequity. In nursing, institutional ethnography (IE) is described as a method of inquiry for taking sides in order to expose socially organized practices of power. Starting from the standpoints of HE policy advocates we explain the methods of IE, focusing on a stepwise description of theoretical and practical applications in the area of policymaking. Results are discussed in the context of three thematic areas: 1) bounding HE talk within biomedical imperialism, 2) situating racialization and marginalization as a subaltern space in HE discourses, and 3) activating HE texts as ruling relations. We conclude with key points about our insights into the methodological and theoretical potential of critical policy research using IE to analyze the social organization of power in HE policy narratives. This paper contributes to critical nursing discourse in the area of HE, demonstrating how IE can be applied to disrupt socially organized neoliberal and colonialist narratives that recycle and redeploy oppressive policymaking practices within and beyond nursing.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.018 | 0.043 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".