“Walk me through the final day”: A thematic analysis study on the family caregiver experience of the Medical Assistance in Dying procedure day
Bibliographic record
Abstract
BACKGROUND: Legalization of assisted dying is progressively expanding worldwide. In Canada, the Medical Assistance in Dying Act became law in 2016. As assisted dying regulations evolve worldwide, comprehending its subjective impact and broader consequences, especially on family members, becomes pivotal for shaping practice, policy, and training. AIM: The goal of this study is to understand the experience of family caregivers on the assisted dying procedure day. DESIGN: Qualitative, thematic analysis, research using semi-structured interviews. SETTING/PARTICIPANTS: Family caregivers of patients who received assisted dying in two hospitals in Canada were recruited. Interviews were conducted at least 6 months after patient death. Conceptual saturation was achieved after analyzing 18 interviews. RESULTS: While caregivers expressed gratitude for the availability of Medical Assistance in Dying, they also described the procedure day as potentially jarring and unsettling. We identified five aspects that shaped their experience: attuned support from the clinical team; preparation for clinical details; congruence between the setting and the importance of the event; active participation and ceremony; and pacing and timing of the procedure. Together, these aspects impacted the level of uneasiness felt by caregivers on the procedure day. CONCLUSIONS: This study emphasized the importance of a family-centered approach to delivering Medical Assistance in Dying. It underscored recognizing the needs of family caregivers during the procedure day and offering strategies to ease their experience. Healthcare providers in jurisdictions where assisted dying is legal or deliberated should consider the applicability of these findings to their unique context.
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.
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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".