Canadian nursing students’ understanding, and comfort levels related to Medical Assistance in Dying
Bibliographic record
Abstract
Background: Recent legislation regarding medical assistance in dying (MAiD) has important implications for nursing practice and education. It follows that Canadian nursing programmes must integrate theoretical and clinical practice related to MAiD in current curricula. Understanding student knowledge and comfort regarding MAiD provides important direction for developing curricula. Purpose: To explore the understanding and comfort levels of BSN students regarding MAiD. Methods: An applied health research methodology drawing on Interpretive Description was employed. Forty BSN students were surveyed before receiving MAiD education delivered through coursework and clinical experiences; 32 students participated in post intervention surveys; six students participated in individual interviews. Results: Three major themes emerged from data analysis: Prior Experience and Lack of Experience; Personal Beliefs and Role Challenges; Need for Knowledge. Student understandings and comfort level levels related to MAiD were strongly influenced by their previous experiences, personal values and beliefs, and knowledge of MAiD policies and practices. Both theory courses and clinical experiences related to MAiD had positive impacts on students’ knowledge and comfort levels. Conclusions: Study findings draw attention to the need for improved education related to end of life and MAiD through both theory courses and clinical practicum experiences to improve student knowledge and comfort levels.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".