P-4 Bridging the gap: understanding the divide between those who consider and those who receive medical assistance in dying
Bibliographic record
Abstract
Introduction Medical Assistance in Dying (MAiD) allows eligible individuals to access medical interventions to end their lives when facing an advanced, irreversible condition accompanied by unbearable suffering.1 2 Many individuals who seek a MAiD may not receive MAiD, due to the lengthy and complex nature of the MAiD process.3 We aimed to understand the differences between participants who considered MAiD but did not undergo the procedure and those who underwent a MAiD. Methods We conducted a secondary analysis of decedent interview data from the Canadian Longitudinal Study on Aging (CLSA) in Canada. Next of kin and proxies of deceased CLSA participants were interviewed about end-of-life characteristics and MAiD considerations for participants who died between June 6, 2016, and March 15, 2022. We examined clinical and demographic characteristics and their association with considering MAiD compared to receiving MAiD. We conducted a descriptive analysis comparing non-MAiD deaths to MAiD-related deaths. Regression methods identified the association between demographic and EoL characteristics factors with consideration and reception of MAiD. Results There was a total of 981 deceased participants with a completed decedent interview. Approximately 25.4% considered MAiD and 6.7% experienced MAiD. In both groups, most participants were male, married, and died of cancer. Considering MAiD was more likely if individuals died in hospice or palliative care (OR 1.73; CI 1.12–2.67), had health care or end-of-life arrangements (OR 1.75; CI 1.15–2.76), and experienced peace with dying (OR 1.87; CI 1.23–2.92). For those who had a MAiD, they were less likely receive palliative care, but had a better overall quality of death and dying experience. Individuals considering MAiD reported dying in place (64.7 vs 56.3; SD: 0.75) and peace with dying (78.3 vs 63.7; SD 0.77) more frequently than those who did not consider MAiD. Discussion Given that more than a quarter of older adults are considering MAiD, honest and informed conversations between health care providers and patients regarding MAiD need to become a part of the EoL care planning process.4 Palliative care settings may offer effective symptom management and psychosocial support that may alleviate the need for MAiD. Considering MAiD as an end-of-life care pathway, even if not received, enhances the overall quality of the dying experience, by providing autonomy during the end-of-life decision-making process contributing to a positive death experience.5 6 References Downar J, Fowler RA, Halko R, Huyer LD, Hill AD, Gibson JL. Early experience with medical assistance in dying in ontario, Canada: a cohort study. CMAJ. 2020;192.E173-E81. An act to amend the criminal code and to make related amendments to other acts (Medical Assistance In Dying) (S.C. 2016 c. Martin S. A good death: Making the most of our final choices. Toronto CHC. Mathews JJ, Hausner D, Avery J, Hannon B, Zimmermann C, Al-Awamer A. Impact of medical assistance in dying on palliative care: a qualitative study. Palliat Med. 2021;35:447–54. (https://www.canada.ca/en/health-canada/services/publications/health-system-services/annual-report-medical-assistance-dying-2022.html) SCFaroMAiDiC. https://www.canada.ca/en/health-canada/services/publications/health-system-services/annual-report-medical-assistance-dying-2022.html. SCFaroMAiDiCN-. Canada S. Medical assistance in dying, 2021. (https://www150.statcan.gc.ca/n1/daily-quotidien/230213/dq230213c-eng.htm). 2023.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".