Exploring Medical Assistance in Dying (MAiD)-Related Grief and Bereavement Through Virtual Digital Storytelling Workshops
Bibliographic record
Abstract
The purpose of this digital storytelling project, conducted entirely in virtual spaces, was to contribute to an enhanced understanding of the experience of family and friends who have accompanied someone who chose to access medical assistance in dying (MAiD) through the creation of digital stories. Participants were recruited through two MAiD-specific support networks, Bridge C-14 and MAID Family Support Society, and represented three Canadian provinces: Alberta, British Columbia, and Ontario. Analysis of the data identified three main themes and several sub-themes. These were understood within the contexts of two overarching categories: MAiD-specific grief and bereavement factors and elements of digital storytelling workshops conducted solely in virtual spaces. The three main themes identified were: (1) motivation for sharing digital stories; (2) the emotional and technological preparedness for the virtual digital storytelling workshops; and (3) the transformative possibilities for both creator and audience of the stories. MAiD-bereaved digital storytellers in this study wanted to create authentic stories about their person who chose MAiD and wished to do so within spaces that provided the appropriate amount of emotional and technological support. This combination was understood to be integral to participants’ ultimate goal: the production of stories that would ideally garner understanding and empathy and provide education in the face of falsehoods and disinformation about MAiD.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".