Psychosocial determinants of nurses’ intention to practise euthanasia in palliative care
Bibliographic record
Abstract
BACKGROUND: Most studies on euthanasia fail to explain the intentions of health professionals when faced with performing euthanasia and are atheoretical. RESEARCH OBJECTIVE: The purpose of this study was to identify the psychosocial determinants of nurses' intention to practise euthanasia in palliative care if it were legalised. RESEARCH DESIGN: A cross-sectional study using a validated anonymous questionnaire based on an extended version of the Theory of Planned Behaviour. PARTICIPANTS AND RESEARCH CONTEXT: A random sample of 445 nurses from the province of Quebec, Canada, was selected for participation in the study. ETHICAL CONSIDERATIONS: The study was reviewed and approved by the Ethics Committee of the Centre hospitalier universitaire de Québec. FINDINGS: The response rate was 44.2% and the mean score for intention was 4.61 ± 1.90 (range: 1-7). The determinants of intention were the subjective (odds ratio = 3.08; 95% confidence interval: 1.50-6.35) and moral (odds ratio = 2.95; 95% confidence interval: 1.58-5.49) norms. Specific beliefs which could discriminate nurses according to their level of intention were identified. DISCUSSION: Overall, nurses have a slightly positive intention to practise euthanasia. Their family approval seems particularly important and also the approval of their medical colleagues. Nurses' moral norm was related to beneficence, an ethical principle. CONCLUSION: To our knowledge, this is the first study to identify nurses' motivations to practise euthanasia in palliative care using a validated psychosocial theory. It also has the distinction of identifying the ethical principles underlying nurses' moral norm and intention.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".