Community implementation of the 3 Wishes Project: an observational study of a compassionate end-of-life care initiative for critically ill patients
Bibliographic record
Abstract
BACKGROUND: The 3 Wishes Project (3WP) promotes a personalized dying experience by eliciting and facilitating individualized terminal wishes for patients, families and the clinicians caring for them. We aimed to evaluate the adaptability of the 3WP to a community intensive care unit (ICU), and to describe the patients cared for with this palliative approach, as well as local implementation strategies. METHODS: The 3WP was implemented in a 15-bed community hospital ICU in southern Ontario from 2017 to 2019. In this observational, descriptive study, we invited adult patients (≥ 18 yr) whose risk of death was deemed to be 95% or greater by the attending physician, or patients undergoing withdrawal of life-support to participate. We abstracted patient data from medical records, as well as the type, timing and cost of each wish, which person or service made and facilitated each wish, and if and why wishes were completed or not. We summarized data both narratively and quantitatively. RESULTS: The 3WP helped to realize 479 (99.2%) of 483 terminal wishes for 101 dying patients. This initiative was introduced as an interprofessional intervention and championed by nursing staff who were responsible for most patient enrolment and wish facilitation. Wishes included humanizing the ICU environment for the patient with belongings and blankets, musical performances, smudging and bathing ceremonies, and keepsakes. The cost was $5.39 per patient (standard deviation $22.40), with 430 (89.8%) wishes incurring no cost. Wishes made directly by patients accounted for 30 (6.2%) of wishes; those from family members and ICU staff accounted for 236 (48.9%) and 238 (49.3%) of wishes, respectively. The program comforted patients and their loved ones, motivating clinicians to sustain this end-of-life intervention. INTERPRETATION: We documented successful implementation of the 3WP in a community hospital, showing program adaptability and uptake outside of academic centres at relatively low cost. The lack of strict protocolization and personalized design of this intervention underscores its inherent flexibility, with potential to promote individualized end-of-life care in nonacademic hospital wards, homes or hospice.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".