Comparing end-of-life practices in different policy contexts: a scoping review
Bibliographic record
Abstract
OBJECTIVES: End-of-life policy reforms are being debated in many countries. Research evidence is used to support different assumptions about the effects of public policies on end-of-life practices. It is however unclear whether reliable international practice comparisons can be conducted between different policy contexts. Our aim was to assess the feasibility of comparing similar end-of-life practices in different policy contexts. METHODS: This is a scoping review of empirical studies on medical end-of-life practices. We developed a descriptive classification of end-of-life practices that distinguishes practices according to their legal status. We focused on the intentional use of lethal drugs by physicians because of international variations in the legal status of this practice. Bibliographic database searches were supplemented by expert consultation and hand searching of reference lists. The sensitivity of the search strategy was tested using a set of 77 articles meeting our inclusion criteria. Two researchers extracted end-of-life practice definitions, study methods and available comparisons across policy contexts. Canadian decision-makers were involved to increase the policy relevance of the review. RESULTS: In sum, 329 empirical studies on the intentional use of lethal drugs by doctors were identified, including studies from 19 countries. The bibliographic search captured 98.7% of studies initially identified as meeting the inclusion criteria. Studies on the intentional use of lethal drugs were conducted in jurisdictions with permissive (62%) and restrictive policies (43%). The most common study objectives related to the frequency of end-of-life practices, determinants of practices, and doctors' adherence to regulatory standards. Large variations in definitions and research methods were noted across studies. The use of a descriptive classification was useful to translate end-of-life practice definitions across countries. A few studies compared end-of-life practice in countries with different policies, using consistent research methods. We identified no comprehensive review of end-of-life practices across different policy contexts. CONCLUSIONS: It is feasible to compare end-of-life practices in different policy contexts. A systematic review of international evidence is needed to inform public deliberations on end-of-life policies and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".