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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".