Socioeconomic Status and Medical Assistance in Dying: A Regional Descriptive Study
Bibliographic record
Abstract
Objective: Concerns that medical assistance in dying (MAiD) may harm vulnerable groups unable to access medical treatments and social supports have arisen since the legalization of MAiD on June 17, 2016; however, there is little research on the topic. The purpose of this study is to investigate the socioeconomic status (SES) of patients who request MAiD at the London Health Sciences Centre (LHSC). Methods: A retrospective analysis of patients from the LHSC MAiD database between June 6, 2016 and December 20, 2019 was conducted. Patients were linked to income data from the 2016 Canadian Census, and their corresponding income quintile was a proxy for SES. Geographic information system (GIS) mapping software was used to visualize the distribution of income and MAiD requests. Results: 39.4% of the LHSC catchment area was classified as low SES. Two hundred thirty-seven (58.1%) MAiD requests came from low SES patients and 171 (41.9%) requests came from high SES patients. Two hundred fifty-nine (63.5%) patients who requested a MAiD assessment did not receive MAiD following their request. Of the 237 lower SES patients, 150 (63.3% [95% CI 57.2-69.3]) did not receive MAiD. Of the 171 higher SES patients, 109 (63.7% [95% CI 56.5-70.9]) did not receive MAiD. Conclusion: A disproportionate number of requests for a MAiD assessment at LHSC came from lower SES patients; however, similar proportions of patients who requested MAiD from each SES group received aid in dying. Future research should explore why a disproportionately high number of low SES patients request MAiD at LHSC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".