A qualitative study exploring family caregivers’ support needs in the context of medical assistance in dying
Bibliographic record
Abstract
OBJECTIVES: Family members are often involved in the provision of care to a relative at some point in their life. Their role becomes inherently complex when their care recipient is interested in seeking medical assistance in dying (MAID). As assisted death for "grievous and irremediable conditions" was legalized in Canada in 2016, the perspectives of family caregivers have received little attention. To best support caregivers to individuals seeking assisted dying, healthcare practitioners must first understand the perspectives of family caregivers in this context. The objective of this qualitative study was to explore the experiences and support needs of family caregivers who are or who have provided care to individuals who are seeking or have sought MAID. METHODS: This study employed a qualitative descriptive design. Family caregivers supporting individuals living with grievous and irremediable conditions were recruited through social media outlets and support organizations. Data were collected through semi-structured telephone interviews and online surveys. Data were transcribed and analyzed using thematic analysis. RESULTS: The study included 11 participants, comprising spouses, parents, and adult children. The research identified three prevalent themes: the caregiver experience including roles and responsibilities and the impact of their role; the MAID experience including the process and their thoughts and feelings about MAID; and caregiver insight into supports and services viewed as valuable or needed for the MAID process. SIGNIFICANCE OF RESULTS: Study findings may assist in the provision and development of best practice resources and guidelines to support healthcare professionals involved in the delivery of MAID. Specifically, caregivers need to be supported in the context of their caregiving responsibilities to minimize the impact on their own lives and optimize their MAID experience.
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".