The impact of medical assistance in dying (MAiD) education on the knowledge and the beliefs of students in a french undergraduate nursing program
Bibliographic record
Abstract
Medical Assistance in Dying (MAiD) can bring out positive and negative emotions in \nnurses. Nurses must feel comfortable with their role, be knowledgeable, and practice their skills \naccording to the laws and regulations. Literature shows that education positively impacts nursing \nstudents' knowledge, beliefs, and comfort about MAiD. Although Canadian nurses' have been \ninvolved with MAiD since 2016, education in undergraduate programs wasn’t implemented until \n2020. This study explored the impact of an education program about MAiD on the knowledge \nand beliefs of students in the French undergraduate nursing program. A longitudinal quasiexperimental design was used, which was informed by the Theoretical Domains Framework. \nFindings showed that some sociodemographic factors were significantly associated with \nknowledge levels. The educational intervention resulted in an increase in knowledge and \nretention, and impacted beliefs. These results can inform nursing practice, future research, as \nwell as policy development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".