A Retrospective Review of Medically Assisted Deaths in Nova Scotia: What Do We Know and Where Should We Go?
Bibliographic record
Abstract
Background: Since legalization of medical assistance in dying (MAiD) in Canada on June 17, 2016, there has been limited information regarding how outcomes of those requesting MAiD relates to comorbidity, social circumstances, geographic location, and access to care. This study aims to identify characteristics associated with the completion of MAiD, once requested, with specialist palliative care (SPC) as the primary exposure of interest. Methods: This retrospective cohort study consists of all patients in Nova Scotia who requested MAiD between June 17, 2016 and December 31, 2018 and were deceased at the time of analysis ( n = 383). Descriptive statistics and logistic regression were performed. Results: A smaller proportion of patients who completed MAiD were seen in consultation by SPC (69.4% vs. 81.1%, p = 0.01). SPC was associated with decreased odds of completing MAiD (odds ratio [OR] 0.59, 95% confidence interval [CI] 0.36–0.96, p = 0.04), as was cohabitation (OR 0.64, 95% CI 0.41–0.99, p = 0.05) and Charlson comorbidity index >6 (OR 0.64, 95% CI 0.41–0.99, p = 0.05). Interpretation: SPC consultation was associated with significantly reduced likelihood of MAiD completion. Contributing factors may include differences in access to SPC services, those completing MAiD being more likely to decline a consultation with SPC, or the impact of SPC upon patient preferences and access to MAiD. While the interface between SPC and MAiD is in its early stages, our findings are supportive of a more integrated approach to coordination of SPC and MAiD services.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".