Physician and Nurse Practitioner Attitudes on Medical Aid in Dying in Long Term Care Settings: A Qualitative Study
Bibliographic record
Abstract
Medical Aid in Dying (MAiD) was decriminalized in Canada with the implementation of Bill C-14 in February of 2016. In the ensuing months and years, a number of discussions and court challenges have clarified the approach to Medical Aid in Dying, and has resulted in a significant number of procedures being completed. Data from the Office of the Chief Coroner in Ontario, highlights that there has been 17 556 MAiD deaths in Ontario since 2016, with 3824 deaths occurring in 2023 thus far. The vast majority of these procedures have occurred in an individual’s home or hospital. Data from the Ontario Long Term Care Association, highlights that 1 in 5 seniors over the age of 80 require long term care placement. The community of residents residing in Long Term Care, is growing. Though currently not well understood, the intersection of Medical Aid in Dying and Long Term Care if of great research interest. LTC homes have thoroughly trained staff to help residents with goals of care conversations, and have become quite expert in supporting residents with their palliative care needs. However, there is a lack of guidelines and policy support when discussions regarding Medical Aid in Dying are identified. The team will interview physicians and nurse practitioners who work in LTC in Ontario to understand their experience with Medical Aid in Dying. Our project will also scope any publically available policies or workflows related to MAID in LTC facilities in the Thames Valley Region (Middlesex, Elgin and Oxford County).
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".