3 Wishes Without Borders: Enhancing End of Life Care for Hospitalized Patients
Bibliographic record
Abstract
Background The 3 Wishes Project (3WP) was developed at St. Joseph’s Healthcare Hamilton to humanize the dying process by engaging patients and families in last wishes. Our objective was to pilot the 3WP on medical wards to gauge interest in expanding outside the intensive care unit (ICU) where it began. Methods We enrolled medical patients who had a high probability of dying during their admission, eliciting, implementing, and documenting terminal wishes. We analyzed data descriptively. Results From January 2017 to March 2020, we enrolled 23 patients and elicited 117 wishes (mean five wishes/patient). Direct engagement in the wish process was possible for 57% of patients. Common wish categories were: facilitating connections, family care, and humanizing the environment. The mean cost/patient was $16; 85% of wishes incurred no cost to the program. Conclusions The initial patients garnered sufficient interest to warrant further expansion. The 3WP was affordable and characterized by increased patient engagement.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".