The quest for a better performing health system: Public expertise and corporate management recipes in France
Bibliographic record
Abstract
Though the government pledged to cut the public deficit from 7.7% of the gross domestic product in 2010 to 3% by 2013, thereby responding to EU Normative power, health expenditures continue to rise, because public demands are higher and more social problems are handled in the health care setting. With French budget deficit threatening France's credit rating, novel instruments were needed. These included corporate management recipes (e.g., pay for performance contracts, patient volume targets, and management by objectives), new compensation mechanisms (e.g., activity‐based accounting and a nationwide scale of health care costs) and far‐reaching laws (e.g., the 2009 HPST bill). Our approach investigates some critical elements of the French health care system. We focus on primary (e.g., family physicians and General Practitioners) and secondary (e.g., hospital and specialty) care. We explore how policies such as the standardization of health services, the regrouping of health policy decisions within the larger Regional Health Agencies, affected citizens' engagement and physicians' autonomy. A French welfare elite pursued a hybrid strategy, regulating quasi‐markets of care providers in a postcompetitive government, while creating supportive conditions for a vibrant private hospital sector. Reforms also emphasized evidenced‐based policy, outputs‐rather than outcome‐measurement, and performance evaluation in a bid to streamline the delivery of health services.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".