P-14 The best death I’ve ever witnessed: examining responses to new narratives around end of life choice
Bibliographic record
Abstract
Background Dr David Juurlink recently used Twitter (18 May 2019) to describe why one of his patients requested assistance to die under Canada’s Medical Aid in Dying (MAiD) law. Dr Juurlink’s tweet has over 31k retweets, 71k likes and 1.2k comments. Aims To analyse responses to Dr Juurlink’s tweet. Methods A thematic analysis of responses to Dr Juurlink’s tweet was conducted. Results Responses were overwhelmingly positive. Expressing gratitude: ‘…Thank you for being open-minded and respectful of her and her families wishes. Thank you for easing her pain … thank you to MAiD who gave her dignity and autonomy on her last journey.’ Sharing experiences: ‘Thank you for sharing... I painfully watched my mother take a week to pass away when the end was inevitable. It will haunt me for the rest of my life.’ Segueing into broader end-of-life issues: ‘…your patient was able to bring her life to a close in comfort and with dignity not only because of MAiD, but also her willingness to openly discuss her mortality and wishes with both her MDs and her family …’ ‘As an anesthesiologist (who is often the first to have a goals of care discussion with a surgical patient) & also as a daughter of a father who died in an ICU w the support of #palliativecare, I thnk u... We need to be having these conversations’ And also prompting different perspectives on MAiD: ‘…Hospice is what she needed! It already exists and would have had the same outcome …’ Conclusions As more jurisdictions legalise assisted dying, stories such as Dr Juurlink’s will increasingly enter public discourse. Assisted dying narratives can be used to promote broader conversations around death and dying. Twitter seems a receptive environment for narratives to be shared and discussed.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".