Resident reflections on end-of-life education: a mixed-methods study of the 3 Wishes Project
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to describe residents' experiences with end-of-life (EOL) education during a rotation in the intensive care unit (ICU), and to understand the possible influence of the 3 Wishes Project. DESIGN: We enrolled dying patients, their families and 1-3 of their clinicians in the 3 Wishes Project, eliciting and honouring a set of 3 wishes to bring peace to the final days of a critically ill patient's life, and ease the grieving process for families. We conducted semistructured interviews with 33 residents who had cared for 50 dying patients to understand their experiences with the project. Interviews were recorded, transcribed verbatim, then analysed using a qualitative descriptive approach. SETTING: 21-bed medical surgical ICU in a tertiary care, university-affiliated hospital. RESULTS: 33 residents participated from internal medicine (24, 72.7%), anaesthesia (8, 24.2%) and laboratory medicine (1, 3.0%) programmes in postgraduate years 1-3. 3 categories and associated themes emerged. (1) EOL care is a challenging component of training in that (a) death in the ICU can invoke helplessness, (b) EOL education is inadequate, (c) personal connections with dying patients is difficult in the ICU and (d) EOL skills are valued by residents. (2) The project reframes the dying process for residents by (a) humanising this aspect of practice, (b) identifying that family engagement is central to the dying process, (c) increasing emotional responsiveness and (d) showing that care shifts, not stops. (3) The project offers experiential education by (a) intentional role modelling, (b) facilitating EOL dialogue, (c) empowering residents to care in a tangible way and (d) encouraging reflection. CONCLUSIONS: For residents, the 3 Wishes Project integrated many forms of active learning for residents. Practice-based rather than classroom-based programmes may engage trainees to develop EOL skills transferable to other settings.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".