Rural healthcare professionals’ participation in Medical Assistance in Dying (MAiD): beyond a binary decision
Bibliographic record
Abstract
Abstract Background Medical Assistance in Dying (MAiD) was legalized in Canada in 2016 and amended in 2021. At the time that this study was conducted, the federal government was considering expanding the eligibility criteria to include patients whose death was not reasonably foreseeable. The purpose of this study was to better understand rural healthcare professionals’ experiences with assisted dying set against the backdrop of legislative expansion. Methods A qualitative exploratory study was undertaken with general rural practice physicians, nurse practitioners, registered nurses, ethicists, patients, and patient families in rural Southern Alberta, Canada. For this paper, data from 18 audio-recorded and transcribed semi-structured interviews with healthcare professionals were analyzed using thematic analysis. Categories and patterns of shared meaning that linked to an overarching theme were identified. Results Between the binary positions of full support for and conscientious objection to assisted dying, rural healthcare professionals’ decisions to participate in MAiD was based on their moral convictions, various contextual factors, and their participation thresholds. Factors including patient suffering; personal and professional values and beliefs; relationships with colleagues, patients and family, and community; and changing MAiD policy and legislation created nuances that informed their decision-making. Conclusions The interplay of multiple factors and their degree of influence on healthcare professionals’ decision-making create multiple decision points between full support for and participation in MAiD processes and complete opposition and/or abstention. Moreover, our findings suggest evolving policy and legislation have the potential to increase rural healthcare professionals’ uncertainty and level of discomfort in providing services. We propose that the binary language typically used in the MAiD discourse be reframed to reflect that decision-making processes and actions are often fluid and situational.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".