Recommendations from the Salzburg Global Seminar on Rethinking Care Toward the End of Life
Bibliographic record
Abstract
OBJECTIVE: In December 2016, 66 health leaders from 14 countries convened at the Salzburg Global Seminar (SGS) to engage in cross-cultural and collaborative discussions centered on 'Rethinking Care Toward the End of Life'. Conversations focused on global perspectives on death and dying, challenges experienced by researchers, physicians, patients and family caregivers. This paper summarizes key findings and recommendations from SGS. DESIGN: Featured sessions focused on critical issues of end of life care led by key stakeholders, physicians, researchers, and other global leaders in palliative care. Sessions spanned across several critical themes including: patient/family/caregiver engagement, integrating health and community-based social care, eliciting and honoring patient preferences, building an evidence base for palliative care, learning from system failures, and delivering end of life care in low-resource countries. Sessions were followed by intensive collaborative discussions which helped formulate key recommendations for rethinking and ultimately advancing end of life care. RESULTS: Prominent lessons learned from SGS include learning from low-resource countries, development of evidence-based quality measures, implementing changes in training and education, and respecting the personal agency of patients and their families. CONCLUSION: There is a global need to rethink, and ultimately revolutionize end of life care in all countries. This paper outlines key aspects of end of life care that warrant explicit improvement through specific action from key stakeholders.
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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 | 0.042 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".