Palliative care competencies for geriatricians across Europe: a Delphi consensus study
Bibliographic record
Abstract
PURPOSE: Integration of palliative care competencies with geriatric medicine is important for quality of care for older people, especially in the last years of their life. Therefore, knowledge and skills about palliative care for older people should be mandatory for geriatricians. The European Geriatric Medicine Society (EuGMS) has launched a postgraduate curriculum for geriatric medicine recently. AIM: Based on this work, the Special Interest Group (SIG) on Palliative care in collaboration with the SIG in Education and Training aimed to develop a set of specific palliative care competencies to be recommended for training at a postgraduate level. METHODS: Competencies were defined using a modified Delphi technique based upon a Likert like rating scale. A template to kick off the first round and including 46 items was developed based on pre-existing competencies developed in Switzerland and Belgium. RESULTS: Three Delphi rounds were necessary to achieve full consensus. Experts came from 12 EU countries. In the first round, the wording of 13 competencies and the content of 10 competencies were modified. We deleted or merged ten competencies, mainly because they were not specific enough. At the end of the 2nd round, one competence was deleted and for three questions the wordings were modified. These modifications had the agreement of the participants during the last round. CONCLUSION: A list of 35 palliative care competencies for geriatricians is now available for implementation in European countries.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".