Narrative medicine and death in the ICU: word clouds as a visual legacy
Bibliographic record
Abstract
OBJECTIVE: The Word Cloud is a frequent wish in the 3 Wishes Project developed to nurture peace and ease the grieving process for dying critically ill patients. The objective was to examine whether Word Clouds can act as a heuristic approach to encourage a narrative orientation to medicine. Narrative medicine is an approach which can strengthen relationships, compassion and resilience. DESIGN: Word Clouds were created for 42 dying patients, and we interviewed 37 family members and 73 clinicians about their impact. We conducted a directed qualitative content analysis, using the 3 stages of narrative medicine (attention, representation, affiliation) to examine the narrative medicine potential of Word Clouds. RESULTS: The elicitation of stories for the Word Cloud promotes narrative attention to the patient as a whole person. The distillation of these stories into a list of words and the prioritisation of those words for arrangement in the collage encourages a representation that did not enforce a beginning, middle or end to the story of the patient's life. Strong affiliative connections were achieved through the honouring of patients, caring for families and sharing of memories encouraged through the creation, sharing and discussion of Word Clouds. CONCLUSIONS: In the 3 Wishes Project, Word Clouds are 1 way that families and clinicians honour a dying patient. Engaging in the process of making a Word Cloud can promote a narrative orientation to medicine, forging connections, making meaning through reminiscence and leaving a legacy of a loved one. Documenting and displaying words to remember someone in death reaffirms their life.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".