Information sur la sécurité des soins : le cas des infections nosocomiales
Bibliographic record
Abstract
Nosocomial infections are a reality that has long been known to healthcare professionals. The fight against this scourge became a central component of health safety policy in France and in Quebec fairly recently. However, it only acquired paramount importance when the effectiveness of existing systems began to be publicly questioned by the media. Through their particular way of presenting a set of dramatic situations faced by patients who contract a nosocomial infection, the media have contributed to the emergence of a new health scandal. The general structure of the nosocomial infection control systems stemming from debates appears to be quite similar in France and in Quebec; however, the development and effectiveness of these systems are not the same. Moreover, striking discrepancies exist between procedures for compensating patients affected by nosocomial infections. Beyond differences in the professional world, the hypothesis defended in this article is that this contrast can be explained, on the one hand, by the way the collective organization of nosocomial infection control has been constructed and, on the other hand, by the way victims’ associations have been able to interact with professionals and the administration since, in France, their proactive involvement has benefited from a more favourable social, professional and political environment. Abstract
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".