Trainee Distress When Faced with End-of-Life Care in Neurology: A Qualitative Analysis
Bibliographic record
Abstract
ABSTRACT: Objective: To identify sources of distress experienced by trainees when providing neuropalliative care and to explore the perceived and unperceived educational needs of trainees learning to deliver neuropalliative care. Method: This study is a post hoc analysis of a qualitative investigation performed at a single Canadian academic center with active clinical services in palliative medicine, neurology, and neurosurgery. Grounded theory methodology was used to explore trainees’ perspectives when learning neuropalliative care. This study used focus groups, using open-ended questions, to elicit participants’ experiences providing neuropalliative care as well as to explore the challenges in neuropalliative care. Results: Qualitative analysis identified multiple sources of distress for trainees in neuropalliative care and broad themes emerged: 1) a lack of experience and knowledge, 2) the emotional toll of learning neuropalliative care, and 3) prognostic uncertainty in neuropalliative care. Conclusion: Our results suggest that palliative neurology curricula should focus not only on symptom management but also on strategies for improving communication about prognosis and managing clinical uncertainty. Improving trainee comfort and confidence in neuropalliative care throughout the illness trajectory may alleviate sources of distress during training and increase quality of care.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".