“We Can All Learn Together so We’re All on the Same Page”: Impact of a Learning Essential Approaches to Palliative Care Hospital Course on Hospitalists’ Practice
Bibliographic record
Abstract
Background: In Canada, access to palliative care varies across jurisdictions. Many health care professionals lack core palliative care competencies. To help build capacities, a pilot education program was conducted at a community hospital in Southwestern Ontario (Canada). Using Pallium Canada's Learning Essential Approaches to Palliative Care (LEAP) Hospital course, generalist hospital physicians participated in this initiative. The purpose of this investigation was to explore the impact of the LEAP Hospital course on physician attitudes, comfort, and care delivery. Methods: Within a Plan-Do-Study-Act framework, a mixed-methods design summarized post-course evaluations, commitment to change (CTC) statements, and interviews with learners and hospital staff. Participants completed pre-course knowledge and post-participation surveys. Group and individual interviews were conducted with learners and staff who practiced alongside LEAP learners. Descriptive statistics were completed for aggregate survey data. Thematic analysis was conducted to summarize learner and staff experiences. Results: Twenty-nine physicians completed the LEAP Hospital course. Ninety-six CTC statements identified improvements in symptom management and communication. Sixteen participants participated in interviews. Learners and hospital staff noted the impact courses had on symptom and disease management and enhanced communication when discussing goals of care. Learners valued case-based learning and connecting with peers who are often siloed in practice. Participants also noted the inclusion of allied health for greater collaboration. Conclusions: LEAP Hospital courses enhanced knowledge and skills and incorporated a greater palliative approach to care. Establishing a community of practice to address educational needs and strategies should be considered while supporting the inclusion of new graduates and hires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".