Patients' and Caregivers' Suggestions for Improving Assisted Dying Regulation: A Qualitative Study in Australia and Canada
Bibliographic record
Abstract
INTRODUCTION: Assisted dying (AD) has been legalised in a small but growing number of jurisdictions globally, including Canada and Australia. Early research in both countries demonstrates that, in response to access barriers, patients and caregivers take action to influence their individual experience of AD, as well as AD systems more widely. This study analyses how patients and caregivers suggest other decision-makers in AD systems should address identified issues. METHODS: We conducted semistructured, qualitative interviews with patients and caregivers seeking AD in Victoria (Australia) and three Canadian provinces (British Columbia, Ontario and Nova Scotia). Data were analysed using reflexive thematic analysis and codebook template analysis. RESULTS: Sixty interviews were conducted with 67 participants (65 caregivers, 2 patients). In Victoria, this involved 28 interviews with 33 participants (32 caregivers, 1 patient) about 28 patient experiences. In Canada, this involved 32 interviews with 34 participants (33 caregivers, 1 patient) about 33 patient experiences. We generated six themes, corresponding to six overarching suggestions by patients and caregivers to address identified system issues: (1) improved content and dissemination of information about AD; (2) proactively develop policies and procedures about AD provision; (3) address institutional objection via top-down action; (4) proactively develop grief resources and peer support mechanisms; (5) amend laws to address legal barriers; and (6) engage with and act on patient and caregiver feedback about experiences. CONCLUSION: AD systems should monitor and respond to suggestions from patients and caregivers with firsthand experience of AD systems, who are uniquely placed to identify issues and suggestions for improvement. To date, Canada has responded comparatively well to address identified issues, whereas the Victorian government has signalled there are no plans to amend laws to address identified access barriers. This may result in patients and caregivers continuing to take on the burdens of acting to address identified issues. PATIENT OR PUBLIC CONTRIBUTION: Patients and caregivers are central to this research. We interviewed patients and caregivers about their experiences of AD, and the article focuses on their suggestions for addressing identified barriers within AD systems. Patient interest groups in Australia and Canada also supported our recruitment process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".