P.082 An evidence-based supportive and palliative care curriculum for Canadian neurology residents
Bibliographic record
Abstract
Background: Graduating residents require general palliative care skills. In Canada, there is no standardized palliative care curriculum for specialty trained residents. The objective of this research is to develop an evidence-based palliative care curriculum designed to provide neurology residents with the general palliative care skills required for providing patient care along the continuum of life. Methods: A needs assessment was performed in Neurology at Western University using qualitative analysis techniques. Residents completed the following:. A curricular outline was developed based on the Kolb learning style inventory (LSI), a knowledge pre-test, the Palliative Medicine Comfort and Confidence Survey and a review of the literature. Two iterations of the curriculum have been developed. Results: Residents identified a need for additional training in supportive and palliative care skills. Based on the Kolb LSI, 9/16 (56.3%) of neurology residents are “accommodators”. General principles identified for inclusion included: symptom management, communication, psychosocial aspects of care, care coordination and access, and myths and pitfalls in palliative care. Conclusions: This project is designed to identify the current palliative educational needs for Neurology residents. The results suggest that specialty trained residents are receptive to embedding training in the principles of palliative care within their training programs.
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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".