End-of-Life Skills and Professionalism for Critical Care Residents in Training: The ESPRIT Survey
Bibliographic record
Abstract
End-of-life (EOL) care is a key aspect of critical care medicine (CCM) training. The goal of this study was to survey CCM residents and program directors (PDs) across Canada to describe current EOL care education. Using a literature review, we created a self-administered survey encompassing 10 CCM national objectives of training to address: (1) curricular content and evaluation methods, (2) residents’ preparedness to meet these objectives, and (3) opportunities for educational improvement. We performed pilot testing and clinical sensibility testing, then distributed it to all residents and PDs across the 13 Canadian CCM programs. Our response rate was 84.3% overall (77 [81.1%] for residents and 13 [100%] for PDs). Residents rated direct observation, informal advice, and self-reflection as both the top 3 most utilized and perceived most effective teaching modalities. Residents most commonly reported comfort with skills related to pain and symptom management (n = 67, 94.3%; score > 3 on 5-point Likert scale), and least commonly reported comfort with donation after cardiac death skills (n = 26-38; 44.8%-65.5%). Base specialty and time in CCM training were independently associated with comfort ratings for some, but not all, EOL skills. With respect to family meetings, residents infrequently received feedback; however, most PDs believed feedback on 6 to 10 meetings is required for competence. When PD perceptions of teaching effectiveness were compared with resident comfort ratings, differences were most apparent for skills related to pain and symptom management, cultural awareness, and ethical principles. By the end of their first subspecialty training year, PDs expect residents to be competent at most, but not all, EOL skills. In summary, trainees and programs rely on clinical activities to develop competency in EOL care, resulting in some educational gaps. Transitioning to competency-based medical education presents an opportunity to address some of these gaps, while other gaps will require more specific curricular intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".