At What Cost? Framing mental illness in digital news media coverage of Medical Assistance in Dying (MAID)
Bibliographic record
Abstract
This is the final report of the research study entitled 'At What Cost? Framing Mental Illness in Digital News Media Coverage of Medical Assistance in Dying (MAID)'. This report contributes to the recent and growing body of scholarship on the rapidly shifting terrain of MAiD in Canada. Our study aimed to capture how Canadian news media portrays MAID's impending expansion to include mental illness and frames the lives of people diagnosed with mental illness. Further, it aimed to evaluate the sociopolitical risks of current news reporting practices. Our team conducted a critical discourse analysis (CDA) of English-language Canadian news media articles from 2020 to 2024 (n=367) using Factiva to understand how MAID MD-SUMC and people diagnosed with mental illness are rhetorically represented within these texts. Outlining the evolution of MAID legislation in Canada and ongoing debates on whether or not MAiD should be considered a form of suicide, the contextual groundwork for the project is laid out. Several limitations related to the use of Factiva are acknowledged. Our analysis of Canadian news reports underscore how MAiD MD-SUMC is primarily written up as a political news story rather than a health issue. Articles primarily reported on the actions of politicians on Parliament Hill. The voices and everyday experiences of people with lived experience (PWLE) of mental illness were limited. This absence raises concerns about the implications of MAID's expansion for impacted communities. Our textual analysis attended to article headlines, who spoke within the text, the words used to describe MAiD and mental illness, and discussions of death. Findings draw attention to the consequences of mainstream news media coverage that consistently pairs 'mental illness' with 'suffering'. To enhance the quality and inclusivity of MAID MD-SUMC reporting, four recommendations are made. First, it is recommended that MAID be reported on as a health issue rather than political news, to better engage the public in health matters. Second, it is recommended that Canadian media guidelines for MAiD be developed and that impacted communities be involved in the development of those guidelines. Third, emphasis is placed on the need to minimize harm. Avoid equating mental illness with suffering in reports about MAID MD-SUMC in order to prevent harmful stereotypes. Fourth, it is recommended that news outlets and reporters covering MAID consider the photographs they use more carefully, to avoid tropes.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".