Medical Assistance in Dying: A Review of Related Canadian News Media Texts
Bibliographic record
Abstract
Medical assistance in dying (MAiD) was legalized in Canada in 2016. Canadians' opinions on the service are nuanced, particularly as the legislation changes over time. In this paper, we outline findings from our review of representations of MAiD in Canadian news media texts since its legalization. These stories reflect the concerns, priorities, and experiences of key stakeholders and function pedagogically, shaping public opinion about MAiD. We discuss this review of Canadian news media on MAiD, provide examples of four key themes we identified (vulnerability, autonomy, dignity, and human rights), and discuss their implications for health policy and equity. Though key stakeholders share the values of autonomy, dignity, and human rights, they appeal to them in diverse ways, sometimes with conflicting policy demands. These representations offer a useful gauge of how views about MAiD continue to shift alongside changes in federal legislation. These stories can influence related policies, respond to the powerful voices that shape MAiD legislation, and have the potential to change national conversations. Our analysis adds to the existing body of scholarship on MAiD by examining post-Bill C-7 news media, identifying related health equity issues and tensions, and discussing potential impacts of MAiD's representations in news media.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".