Medical assistance in dying in Canada: A scoping review on the concept of suffering
Bibliographic record
Abstract
OBJECTIVES: Medical Assistance in Dying (MAiD) has been legal in Canada since June 2016. A person can receive MAiD if their suffering cannot be relieved under conditions that they consider acceptable. Informed consent requires that the person requesting MAiD has received all the information needed to make their decision; that is, medical diagnosis and prognosis, available treatments including palliative care. The evaluation of unbearable suffering is known to be challenging as suffering is often psychological, existential, and social in nature. While interventions to relieve suffering exist, it is unclear how suffering is assessed and addressed in the literature on MAiD practice. No scoping review exists on the topic in Canada. The aim of this study was to understand how the concept of suffering was approached within the Canadian MAiD grey (GL) and scientific (peer-reviewed) literature (SL), specifically: 1- How suffering is defined and assessed in the context of MAiD in Canada and 2- Which interventions in response to suffering are recommended within the process of obtaining informed consent for MAiD and throughout the process of MAiD itself. METHODS: A scoping review was conducted based on PRISMA-SR guidelines. SL articles (N = 1027) were identified from a review of 6 databases and GL documents (N = 537) were obtained from the provinces of Quebec, Ontario and British Columbia. Documents were analyzed using NVivo with coding by two-raters and continuous team discussions. RESULTS: A multidimensional definition of suffering, akin to the concept of total pain, is used. The assessment of suffering is based upon patients' reports. Tools to aid in the assessment are not comprehensively covered. Specific interventions to address suffering were often focused on active listening and the management of physical symptoms. No specific interventions were mentioned and there was no reference to clinical practice guidelines in the grey literature to address other components of suffering. The use of a multidisciplinary approach is suggested without specifying the nature of involvement. CONCLUSIONS: Our review indicates that published guidelines of MAID assessments could include clearer structure around the assessment and management of suffering, with suggestions of tools that may help clarify types of suffering and reference to clinical practice guidelines and interventions to holistically attend to patient suffering with an attention on non-physical symptoms. Guidelines would benefit from clearer explanations of how members of an interdisciplinary teams could be coherently coordinated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".