Assessing attitudes towards medical assisted dying in Canadian family medicine residents: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Medical Assistance in Dying (MAID) in Canada came into effect in 2016 with the passing of Bill C-14. As patient interest and requests for MAID continue to evolve in Canada, it is important to understand the attitudes of future providers and the factors that may influence their participation. Attitudes towards physician hastened death (PHD) in general and the specific provision of MAID (e.g., causing death by lethal prescription or injection) are unknown among Canadian residents. This study examined residents' attitudes towards PHD and MAID, and identified factors (e.g., demographics, clinical exposure to death and dying) that may influence their decision to participate in PHD and provide MAID. METHODS: A cross-sectional survey was adapted from prior established surveys on MAID to reflect the Canadian setting. All Canadian family medicine programs were invited to participate. The survey was distributed between December 2016 and April 2017. Analysis of the results included descriptive statistics to characterize the survey participants and multivariable logistic regressions to identify factors that may influence residents' attitudes towards PHD and MAID. RESULTS: Overall, 247 residents from 6 family medicine training programs in Canada participated (response rate of 27%). While residents were most willing to participate in treatment withdrawal (52%), active participation in PHD (41%) and MAID by prescription of a lethal drug (31%) and lethal injection (24%) were less acceptable. Logistic regressions identified religion as a consistent and significant factor impacting residents' willingness to participate in PHD and MAID. Residents who were not strictly practicing a religion were more likely to be willing to participate in PHD (OR = 17.38, p < 0.001) and MAID (lethal drug OR = 10.55, p < 0.01, lethal injection OR = 8.54, p < 0.05). Increased clinical exposure to death and dying crudely correlated with increased willingness to participate in PHD and MAID, but when examined in multivariable models, only a few activities (e.g., declaring death, completing a death certificate) had a statistically significant association. Other significant factors included the residents' sex and location of training. CONCLUSIONS: Residents are hesitant to provide MAID themselves, with religious faith being a major factor impacting their decision.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".