Investigating palliative care nurse attitudes towards medical assistance in dying: An exploratory cross‐sectional study
Bibliographic record
Abstract
AIM: To investigate palliative care nurse attitudes towards medical assistance in dying. DESIGN: An exploratory cross-sectional study design. METHODS: A mailed letter recruited participants with data collection occurring on a secure online survey platform between November 2017-February 2018. Data analyses included descriptive and bivariate statistics and stepwise linear regression. RESULTS: Palliative care nurse attitudes towards medical assistance in dying were explained by perceived expertise in the social domain of palliative care, personal importance of religion/faith, professional importance of religion/faith, and nursing designation. CONCLUSION: This study reveals the perceived importance of religion, versus religious affiliation alone, as significant in influencing provider attitudes towards assisted dying. Further research is needed to understand differences in attitudes between Registered Nurses and Registered Practical Nurses and how the social domain of palliative care influences nurse attitude. IMPACT: Organizations must prioritize nursing input, encourage open interprofessional dialogue and provide support for ethical decision-making, practice decisions, and conscientious objection surrounding medical assistance in dying. Longitudinal nursing studies are needed to understand the impact of legislation on quality and person-centred end-of-life care and the emotional well-being/retention of palliative care nurses.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".