Data and cooperative work in Health Services: French specificities and comparisons with other countries
Bibliographic record
Abstract
Abstract: With information and services development, medical data represent a very important economic and social stake. In this paper, we will analyze their more specific aspects: mainly personal, sensitive, confidential and conform to a particular legislation. We will draw from the French experience on health networks and enlarge on comparisons with the United States, Canada, United Kingdom and Spain. The main problem is to set up the information systems with their central element: the patient’s medical record, in articulation with the national health cards. Whom do the collected data belong to? To the patient, to the various physicians or to the organizations (hospital, clinic, health insurance, … ) taken individually? What about the data hosting entity? Another stake is the use of data produced by health networks. Can we separate the use of actual data from their property? How a patient may access to his personal health record (directly, indirectly, through a physician?) and to which data (the whole file or a summary?).We will give our elements of reflection.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".