Rapport d'activités 2024-2025. Plateforme nationale pour la recherche sur la fin de vie.: Un réseau de recherche interdisciplinaire.
Bibliographic record
Abstract
The French National Platform for End-of-Life Research is an interdisciplinary research network dedicated to advancing knowledge on end-of-life issues in France. Since 1 January 2023, the Platform has been fully integrated into the "Maison des sciences de l’homme et de l’environnement (MSHE)" at the University of Franche-Comté. It is co-chaired by philosopher Sarah Carvallo and physician Sadek Beloucif, reflecting the Platform’s commitment to bridging medical sciences with the humanities and social sciences.The Platform continues to serve as a national observatory for research on end-of-life. The national directory of researchers expanded to 434 members in 2023, representing a 10% increase compared to the previous year. The research landscape currently includes 83 active projects addressing themes such as home-based care, bereavement, legal frameworks, and medical sedation. Academic production in the field remains dynamic, with 13 doctoral theses defended during the year and 98 ongoing PhD projects. Research activities remain balanced between medical and life sciences (54%) and the humanities and social sciences (46%).Among its key initiatives, the Platform launched the 2023 Call for Expressions of Interest, focusing on autonomy and disability. Five research projects were selected for funding, including studies on pediatric palliative care and the professional practices of home-care providers. The Platform also organized its 5th Scientific Days in Besançon, dedicated to the role of research in public debate, and hosted several webinars while maintaining international collaboration with the Quebec-based RQSPAL network.The Platform also contributed actively to public policy discussions, particularly in the national debate on end-of-life issues, and participated in the development of the French 2024–2034 decennial strategy for palliative care and pain management.Looking ahead, the Platform plans to redefine its priority research axes, strengthen international cooperation by joining the European Association for Palliative Care (EAPC), and continue supporting early-career researchers through doctoral workshops and potential funding opportunities for Master’s-level research internships.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.098 | 0.055 |
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".