End-of-Life Decision Making: Policy and Statutory Progress (2011-2020)
Bibliographic record
Abstract
In 2009, the Royal Society of Canada (RSC) identified a series of urgent scientific and public policy questions. It established a series of five Expert Panels to study the issues and provide recommendations for next steps. It is now timely to revisit the findings of these Expert Panel Reports. What impact have they had? Have their recommendations been implemented? What are the next steps in terms of policy options?\nTo answer these questions, the RSC is establishing Policy Briefing Committees (PBC) to: describe the context, findings, and recommendations of the report; track policy developments in relation to the panel’s findings and recommendations; and identify future policy challenges and implementation options. \nAn important distinction from the work of each original expert panel is that the PBCs will not undertake reviews of the scientific literature, but instead focus on matters with respect to findings and recommendations issued by the reports and subsequent public policy developments.\nThis Policy Briefing Committee Report examines policy and statutory developments since publication of the RSC’s 2011 Expert Panel Report on End-of-Life Decision Making.
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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 | 0.070 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.021 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".