Hospice Palliative Care (HPC) and Medical Assistance in Dying (MAiD): Results From a Canada-Wide Survey
Bibliographic record
Abstract
BACKGROUND: With the legalization of medical assistance in dying (MAiD) in Canada, physicians and nurse practitioners now have another option within their scope of practice to consider alongside hospice palliative care (HPC) to support the patient and family regardless of their choice toward natural or medically assisted death. To elucidate insights and experiences with MAiD since its inception and to help adjust to this new end-of-life care environment, the membership of the Canadian Hospice Palliative Care Association (CHPCA) was surveyed. METHODS: The CHPCA developed and distributed a 16-item survey to its membership in June 2017, one year following the legalization of MAiD. Data were arranged in Microsoft® Excel and open-ended responses were analyzed thematically using NVivo 12 software. RESULTS: From across Canada, 452 responses were received (response rate: 15%). The majority of individuals worked as nurses (n = 161, 33%), administrators (n = 79, 16%), volunteers (n = 76, 16%) and physicians (n = 56, 11%). Almost 75% (n = 320) of all respondents indicated that they had experienced a patient in their program who had requested MAiD. Participants expressed dissatisfaction with the current psychological and professional support being provided by their health care organization and Ministry of Health - during and after the MAiD procedure. CONCLUSION: The new complexities of MAiD present unique challenges to those working in the health-care field. There needs to be an increased focus on educating/training providers as without proper support, health-care workers will be unable to perform to their full potential/scope of practice while also providing patients with holistic and accessible care.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".