Developing robust systems for Track 2 MAID in Canada: A qualitative study
Bibliographic record
Abstract
Background: Developing robust care systems to support the evolving landscape of Medical Assistance in Dying (MAID) in Canada has proven difficult. The complexity of applicants applying under Track 2, in which a reasonably foreseeable natural death is not required for eligibility, has challenged the capacities of systems that were initially developed to care for Track 1 applicants. Objective: To identify structures and practices described by healthcare providers involved in Track 2 care as key to developing a robust system of high quality and safe care for persons applying under Track 2 MAID in Canada. Design: A qualitative study informed by the principles of Interpretive Description, a pragmatic research approach developed for health disciplines. Methods: Fifty-five healthcare providers, MAID program administrators, and key informants participated in semi-structured interviews. Interviews were conducted over Zoom™, audio-recorded, transcribed, and analysed using strategies outlined in Interpretive Description. Results: The work of Track 2 care was described as complex, emotionally-laden, risky, and in some regions, inadequately remunerated. MAID coordination centres were effective for Track 2 care when they were team-based; had a structured intake that supported assessors and managed the expectations of applicants; and provided education and navigation support for applicants and family. The coordination centre role was particularly critical when applicants had no primary care provider. The availability of prospective interdisciplinary case consultation was considered essential for optimizing care in the context of Track 2 applicants. Conclusion: The assessment for, and provision of, MAID is unique in healthcare. It is the only federally legislated healthcare act, and it is irreversible. It is also morally contentious, particularly in the case with Track 2 where applicants' years of life lost may be significant. The safe and effective implementation of Track 2 requires a robust systems approach that to-date is available in only some regions of Canada.
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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 | 0.008 | 0.007 |
| 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".