Basic Training in Palliative Medicine for Internal Medicine Residents: Pilot Testing of a Canadian Model in Switzerland
Bibliographic record
Abstract
Background: In Switzerland, palliative care (PC) clinical training is well established at undergraduate and specialist postgraduate levels. However, postgraduate nonspecialist training curricula are less documented. Local Problem: A structured curriculum for nonspecialist rotation within internal medicine (IM) in specialized PC wards is lacking. Objective: To pilot two versions of a PC nonspecialist curriculum for IM residents in Swiss PC units. Methods: In the pilot phase, two curricula-short immersion (3-10 weeks, based on the University of Toronto's Internal-Medicine PC Rotation) and standard nonspecialist (11-18 weeks, based on the Canadian Society of Palliative Care Physician Competencies)-were assessed using a mixed-method online survey. One university and two nonuniversity sites participated. The analysis was descriptive. Results: Five residents and eight supervisors of five training rotations (July-October 2023) responded. Overall, curriculum quality and feasibility (content and time) received positive ratings across all groups, with high satisfaction concerning organization, educational design, learning support, climate, experience, and facilities. Nonuniversity sites were generally rated more positively than university sites. Qualitative feedback paralleled these findings, highlighting the curriculum's relevance and fit with learners' needs and suggesting potential simplifications and more personalized planning. Conclusions: Establishing short and standard duration curricula for a PC program is viable and well received by nonspecialist trainees. Future implementation should concentrate on personalized learning objectives and streamlining the content and structure of the competencies. Cooperation within various training settings (university and regional hospitals) as well as on an international level (e.g., Canada-Switzerland) may further improve the quality of the proposed training formats.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".