Mourir chez soi, mourir avec les siens : quels effets d’une prise en charge de la fin de vie à domicile ?
Bibliographic record
Abstract
En France, le recours à l’hospitalisation à domicile (HAD) est en augmentation, même si celle-ci reste une pratique statistiquement marginale. Les situations palliatives et de fin de vie y prennent une place d’importance. Cet article vise à démontrer que ces situations génèrent de nouveaux enjeux dans le processus du mourir. En effet, d’une part, le lieu même où elles se déroulent − le domicile – n’est plus pensé dans notre société comme le lieu du mourir. D’autre part, les acteurs qui y participent, et particulièrement les proches de la personne en fin de vie, doivent ajuster leur nouveau rôle et celui de leurs interlocuteurs (le malade, les soignants professionnels). L’analyse de la mort en HAD mérite ainsi d’être saisie au croisement des logiques médicales et familiales.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".