E.06 Developing and evidence-based palliative care curriculum for neurology resident trainees
Bibliographic record
Abstract
Background: Graduating neurology residents require general palliative care skills. This study aims to develop an evidence-based palliative care curriculum to provide neurology residents with the general palliative care skills required for providing patient care along the continuum of life. Methods: A needs assessment of the palliative skills necessary for a neurology resident was performed. Focus groups were held with physicians, allied health care and senior residents. Semi-structured interviews were held with patients and their caregivers. Interviews analysed using qualitative thematic analysis techniques. The Kolb learning style inventory will determine the learning style of neurology residents and inform the curricular design. Results: Qualitative analysis identified 3 overarching challenges for neurology residents: 1) uncertainty regarding disease trajectory in neurology and timing of palliative care discussions; 2) cohesiveness of the health care team regarding end of life issues; 3) the role of the resident in initiating palliative care. Other principals identified for inclusion were: symptom management, communication, psychosocial aspects of care, care coordination and access, and myths and pitfalls in palliative care. Conclusions: This project will identify the current best evidence and expert opinion in palliative care neurology. The data will be used to develop a novel Canadian neurological palliative care curriculum.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".