P.108 Integrating learner feedback in developing an evidence-based palliative care curriculum for neurology residents
Bibliographic record
Abstract
Background: Palliative care is a cornerstone of the management of progressive neurological illness, but there lacks a standardized evidence-based curriculum to teach the unique aspects of neurology-based palliative care to current learners. Methods: A needs assessment involving focus groups with patients, physicians, interdisciplinary members, and trainees was conducted to identify gaps in the current curriculum. The Kolb Learning Style Inventory identified learning strategies among neurology residents. A Palliative Medicine Comfort and Confidence Survey and knowledge pre-test was distributed to determine current learner needs. The curriculum was delivered during academic time, and feedback was obtained for further content revision. Results: Qualitative analysis was used to develop the curriculum with the key principles of symptom management, end-of life communication, psychosocial components of care, and community coordination. Learning styles varied, but preference for active experimentation and concrete experience was noted. Learners identified as comfortable with withdrawal of medical interventions, but requiring support on home palliative care referral, and management of terminal delirium and dyspnea. Further teaching was requested for end of life ethics and communication skills. Conclusions: By integrating current best evidence-based practice in palliative neurology with learner feedback, this project aims to create a comprehensive palliative care curriculum for neurology learners.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".