When Health Care is Displaced by State Interests: Building Dialogue Through Shared Findings
Bibliographic record
Abstract
Health sociologists interested in how macro state influences affect micro health care practices have much to gain from meta-ethnography research. In this article, we bring together insights from two separate empirical studies on state health care services involving HIV/AIDS as a way to speak to larger issues about the organization and production of medical expertise and governance in health care systems. We use Noblit and Hare's meta-ethnography approach to bring these studies into conversation and identify six shared "organizers" of health care encounters. The organizers illustrate how state health interests operate across institutional contexts and impact the work of providers in seemingly unrelated health care settings. On the basis of this synthesis, we conclude that state interests both structure and create conflict in health care settings. We believe this perspective offers the potential to advance the goals of health sociology and the field of qualitative health research in general.
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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 | 0.202 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.034 | 0.100 |
| Scholarly communication | 0.033 | 0.051 |
| Open science | 0.008 | 0.052 |
| Research integrity | 0.010 | 0.016 |
| 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".