P.143 Exploring end-of-life decision making and perspectives on Medical Assistance in Dying through the eyes of individuals living with cervical spinal cord injuries in Nova Scotia
Bibliographic record
Abstract
Background: Individuals with spinal cord injuries (SCI) are invariably faced with decisions around management of their injury; from life prolonging to palliating interventions. End-of-life (EOL) decision-making has recently come to include conversations around Medical Assistance in Dying (MAID), as legislation changes have expanded access. The intersection between SCI and MAID, and other EOL decision-making has yet to be explored. We sought to discuss awareness and perspectives on MAID and EOL decision-making. Methods: We conducted hour-long semi-structured interviews with 15 individuals living with cervical SCI. Interviews took place over the telephone or virtually, and transcripts were analyzed using an iterative coding process and thematic analysis. Results: There was a global lack of awareness of options, that changed with time as participants assumed more independent roles in decision making. Participants possessed general awareness of MAID, but variable understanding of who legislation applies to. The way individuals with SCI could interact with MAID legislation brought forth interesting discussions around bodily autonomy and self-determination. Some voiced their own desire initially for MAID, while others vacillated or were more strongly opposed. Conclusions: This study emphasizes the importance of engaging with difficult conversations, and striking the balance of respecting autonomy and self-determination, within the constraints of each individual’s situation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".