Physician and Nurse Practitioner Attitudes on Medical Aid in Dying in Long Term Care Settings: A Qualitative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it