Internal Medicine Residents’ Beliefs, Attitudes, and Experiences Relating to Palliative Care: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Internal medicine residents are frequently called upon to provide palliative care to hospitalized patients, but report feeling unprepared to do so effectively. Curricular development to enhance residents' palliative care skills and competencies requires an understanding of current beliefs, attitudes and learning priorities. METHODS: We conducted a qualitative study consisting of semi-structured interviews with ten internal medicine residents to explore their understanding of and experiences with palliative care. RESULTS: All of the residents interviewed had a sound theoretical understanding of palliative care, but faced many challenges in being able to provide care in practice. The challenges described by residents were system-related, patient-related and provider-related. They identified several priority areas for further learning, and discussed ways in which their current education in palliative care could be enhanced. CONCLUSIONS: Our findings provide important insights to guide curricular development for internal medicine trainees. The top five learning priorities in palliative care that residents identified in our study were: 1) knowing how and when to initiate a palliative approach, 2) improving communication skills, 3) improving symptom management skills, 4) identifying available resources, and 5) understanding the importance of palliative care. Residents felt that their education in palliative care could be improved by having a mandatory rotation in palliative care, more frequent didactic teaching sessions, more case-based teaching from palliative care providers, opportunities to be directly observed, and increased support from palliative care providers after-hours.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".