Medical assistance in dying: implications for health systems from a scoping review of the literature
Bibliographic record
Abstract
Objective Medical assistance in dying (MAiD) is the medical provision of substances to end a patient’s life at their voluntary request. While legal in several countries, the implementation of MAiD is met with ethical, legislative and clinical challenges, which are often overshadowed by moral discourse. Our aim was to conduct a scoping review to explore key barriers for the integration of MAiD into existing health systems. Methods We searched electronic databases (CINAHL, Embase, MEDLINE, and PsycINFO) and grey literature sources from 1990 to 2017. Studies discussing barriers and/or challenges to implementing MAiD from a health system’s perspective were included. Full-text papers were screened against inclusion/exclusion criteria for article selection. A thematic content analysis was conducted to summarize data into themes to highlight key implementation barriers. Results The final review included 35 articles (see online Appendix 1). Six categories of implementation challenges emerged: regulatory (n = 26), legal (n = 15), social (n = 9), logistical (n = 9), financial (n = 3) and compatibility with palliative care (n = 3). Within four of the six identified implementation barriers (regulatory, legal, social and logistical) were subthemes, which described barriers related to legalizing MAiD in more detail. Conclusion Despite multiple challenges related to its implementation, MAiD remains a requested end-of-life option, requiring careful examination to ensure adequate integration into existing health services. Comprehensive models of care incorporating multidisciplinary teams and regulatory oversight alongside improved clinician education may be effective to streamline MAiD services.
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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 | 0.060 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".