The reflection room: Shifting from death-avoiding to death-discussing
Bibliographic record
Abstract
Thinking about dying and death is something we tend \nnot to do, and those who promote Advance Care \nPlanning for the health care in ourlast days, hours and \nminutes would like us to do more. However, planning \nrequires us to think about how we want to live our \nfinal days and then share those wishes with others. \nThis research proposes the question: How might we \nuse human-centred design and qualitative research \n \nto go from being a death-avoiding society to a death- \ndiscussing society? \n \nHuman beings are storytellers. Understanding complex \nchallenges through narrative builds empathy. Stories \nalso trigger the imagination for future possibility. We \npropose that providing places for storytelling — and \nplaces for reading the stories of others — might trigger \nmore thinking and break through the social complexity \nthat can be a barrier to discussing dying and death. \nAs part of a year-long research project, we are creating \n“Reflection Rooms” – both short-term physical spaces \nacross Canada and an online website – where people \nare invited to write their stories about dying and death \nand read the stories of others. We will share emerging \nthemes from the research and pose the questions: \nHow might we engage patients and families in shared \n \nstorytelling as they navigate decision-making at end-of- \nlife? How might collective storytelling about dying and \n \ndeath support the design of human-centred Advance \nCare Planning experiences? \n \nAt the RSD5 Conference we also propose to set-up \na Reflection Room pop-up for the duration of the \nconference and invite people to take a few moments to \nreflect on their own experiences with dying and death \nand add them to the reflection wall to continue building \nthe collective story.
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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".