The role of nurses in physician-assisted deaths in Belgium
Bibliographic record
Abstract
BACKGROUND: Belgium's law on euthanasia allows only physicians to perform the act. We investigated the involvement of nurses in the decision-making and in the preparation and administration of life-ending drugs with a patient's explicit request (euthanasia) or without an explicit request. We also examined factors associated with these deaths. METHODS: In 2007, we surveyed 1678 nurses who, in an earlier survey, had reported caring for one or more patients who received a potential life-ending decision within the year before the survey. Eligible nurses were surveyed about their most recent case. RESULTS: The response rate was 76%. Overall, 128 nurses reported having cared for a patient who received euthanasia and 120 for a patient who received life-ending drugs without his or her explicit request. Respectively, 64% (75/117) and 69% (81/118) of these nurses were involved in the physician's decision-making process. More often this entailed an exchange of information on the patient's condition or the patient's or relatives' wishes (45% [34/117] and 51% [41/118]) than sharing in the decision-making (24% [18/117] and 31% [25/118]). The life-ending drugs were administered by the nurse in 12% of the cases of euthanasia, as compared with 45% of the cases of assisted death without an explicit request. In both types of assisted death, the nurses acted on the physician's orders but mostly in the physician's absence. Factors significantly associated with a nurse administering the life-ending drugs included being a male nurse working in a hospital (odds ratio [OR] 40.07, 95% confidence interval [CI] 7.37-217.79) and the patient being over 80 years old (OR 5.57, 95% CI 1.98-15.70). INTERPRETATION: By administering the life-ending drugs in some of the cases of euthanasia, and in almost half of the cases without an explicit request from the patient, the nurses in our study operated beyond the legal margins of their profession.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 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".