Corporate Logic in Clinical Care: The Case of Diabetes Management
Bibliographic record
Abstract
As large corporations come to dominate U.S. health care, clinical medicine is increasingly market-driven and governed by business principles. We examine ways in which health insurers and health care systems are transforming the goals and means of clinical practice. Based on ethnographic research of diabetes management in a large health care system, we argue that together these organizations redefine clinical care in terms that prioritize financial goals and managerial logics, above the needs of individual patients. We demonstrate how emphasis on quality metrics reduces clinical work to quantifiable outcomes, redefining diabetes management to be the pursuit of narrowly defined goal numbers, despite often serious health consequences of treatment. As corporate employees, clinicians are compelled to pursue goal numbers by the heavy emphasis payers and health systems place on quality metrics, and accessing the required medications becomes the central focus of clinical practice.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.053 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".