Anticipated Immigration Rate Changes Necessary to Maintain Canadian Population with Proposed Expansion of Medical Assistance in Dying for Mental Health Disorders
Bibliographic record
Abstract
Recent legalization and normalization of euthanasia through the government program of medical assistance in dying (MAiD) is impacting Canada's population dynamics and is now the fifth leading cause of death. Ongoing proposals suggest expanding MAiD eligibility to mental illness. A knowledge gap exists to determine to what degree this expansion of MAiD may exacerbate the existing challenges associated with an aging population and declining birth rates. It may also complicate meeting national population targets of 100 million by 2100 set by the Century Initiative. To fill these knowledge gaps, this study conducts a comprehensive evaluation of the prevalence of mental illness in Canada. The results show that if MAiD is expanded to mental disorders under the high MAiD scenario by mid-century it will impact about 12.3% of the population. The low MAiD scenario projects an increase from 45,786 to 227,024 additional deaths/year. The likely scenario shows MAiD growing from 214,499 to approximately 896,312 by mid-century. Thus, if MAiD is expanded to mental illness under the likely scenario, more than half a million additional immigrants will be needed per year. These results emphasize the importance of integrating immigration policies with MAiD practices to ensure demographic stability and achieve long-term population objectives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".